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All Journal Tekno : Jurnal Teknologi Elektro dan Kejuruan TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Informatika Harmonia: Journal of Research and Education International Journal of Artificial Intelligence Research INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Knowledge Engineering and Data Science Jurnal Media Elektro : Journal of Electrical Power, Informatics, Telecommunication, Electronics, Computer and Control System ILKOM Jurnal Ilmiah JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Journal of Electronics, Electromedical Engineering, and Medical Informatics Mobile and Forensics International Journal of Visual and Performing Arts Journal of Robotics and Control (JRC) Sains, Aplikasi, Komputasi dan Teknologi Informasi JPP (Jurnal Pendidikan dan Pembelajaran) Frontier Energy System and Power Engineering Science in Information Technology Letters International Journal of Robotics and Control Systems Kaisa: Jurnal Pendidikan dan Pembelajaran ALINIER: Journal of Artificial Intelligence & Applications Fidelity : Jurnal Teknik Elektro SinarFe7 Jurnal Inovasi Teknologi dan Edukasi Teknik Jurnal INFOTEL Karunia: Jurnal Hasil Pengabdian Masyarakat Indonesia

Zaeni, Ilham Ari Elbaith

Universitas Negeri Malang

Author-ID : 512535

Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Library & Information Science Mathematics Mechanical Engineering Neuroscience Social Sciences

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Home Energy Security Prototype using Microcontroller Based on Fingerprint Sensor Nusantar, Alrizal Akbar Nusantar Akbar; Zaeni, Ilham Ari Elbaith; Lestari, Dyah
Frontier Energy System and Power Engineering Vol 1, No 2 (2019): JULY
Publisher : Universitas Negeri Malang

The globalization era brings rapid development in technology.The human need for speed and easiness pushed them toinnovate, such as in the security field. Initially, the securitysystem was conducted manually and impractical compared tonowadays system. A security technology that is developed wasbiometric application, particularly fingerprint. Fingerprintbasedsecurity became a reliable enough system because of itsaccuracy level, safe, secure, and comfortable to be used ashousing security system identification. This research aimed todevelop a security system based on fingerprint biometric takenfrom previous researches by optimizing and upgrading theprevious weaknesses. This security system could be a solutionto a robbery that used Arduino UNO Atmega328P CH340 R3Board Micro USB port. The inputs were fingerprint sensor, 4x5keypad, and magnetic sensor, whereas the outputs were 12 Vsolenoid, 16x2 LCD, GSM SIM800L module, LED, andbuzzer. The advantage of this security system was its ability togive a danger sign in the form of noise when the systemdetected the wrong fingerprint or when it detects a forcedopening. The system would call the homeowner then. Otherthan that, this system notified the homeowner of all of theactivities through SMS so that it can be used as a long-distanceobservation. This system was completed with a push button toopen the door from the inside. The maximum fingerprints thatcould be stored were four users and one admin. The adminâ€™sjob was to add/delete fingerprints, replace the home ownerâ€™sphone number, and change the systemâ€™s PIN. The resultsshowed that the fingerprint sensor read the prints in a relativelyfast time of 1.136 seconds. The average duration that wasneeded to send an SMS was 69 seconds while through call was3.2 seconds.

Pelabelan Kelas Kata Bahasa Jawa Menggunakan Hidden Markov Model Mursyit, Mohammad; Wibawa, Aji Prasetya; Zaeni, Ilham Ari Elbaith; Rosyid, Harits Ar
Mobile and Forensics Vol 2, No 2 (2020)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v2i2.2450

Part of Speech TaggingÂ atauÂ POS TaggingÂ adalah proses memberikan label pada setiap kata dalam sebuah kalimat secara otomatis. Penelitian ini menggunakan algoritmaÂ Hidden Markov ModelÂ (HMM) untuk prosesÂ POS Tagging. Perlakuan untukÂ unknown wordsÂ menggunakanÂ Most Probable POS-Tag.Â DatasetÂ yang digunakan berupa 10 cerita pendek berbahasa Jawa terdiri dari 10.180 kata yang telah diberikanÂ tagsetBahasa Jawa. Pada penelitian ini prosesÂ POS TaggingÂ menggunakan dua skenario. Skenario pertama yaitu menggunakan algoritmaÂ Hidden Markov ModelÂ (HMM) tanpa menggunakan perlakuan untukÂ unknown words. Skenario yang kedua menggunakan HMM danÂ Most Probable POS-TagÂ untukÂ perlakuan unknown words. Hasil menunjukan skenario pertama menghasilkan akurasi sebesar 45.5% dan skenario kedua menghasilkan akurasi sebesar 70.78%.Â Most Probable POS-TagÂ dapat meningkatkan akurasi padaÂ POS TaggingÂ tetapi tidak selalu menunjukan hasil yang benar dalam pemberian label.Â Most Probable POS-TagÂ dapat menghilangkan probabilitas bernilai Nol dariÂ POS Tagging Hidden Markov Model. Hasil penelitian ini menunjukan bahwaÂ POS TaggingÂ dengan menggunakanÂ Hidden Markov ModelÂ dipengaruhi oleh perlakuan terhadapÂ unknown words, perbendaharaan kata dan hubungan label kata padaÂ dataset.Â Â Part of Speech Tagging or POS Tagging is the process of automatically giving labels to each word in a sentence. This study uses the Hidden Markov Model (HMM) algorithm for the POS Tagging process. Treatment for unknown words uses the Most Probable POS-Tag. The dataset used is in the form of 10 short stories in Javanese consisting of 10,180 words which have been given the Javanese tagset. In this study, the POS Tagging process uses two scenarios. The first scenario is using the Hidden Markov Model (HMM) algorithm without using treatment for unknown words. The second scenario uses HMM and Most Probable POS-Tag for treatment of unknown words. The results show that the first scenario produces an accuracy of 45.5% and the second scenario produces an accuracy of 70.78%. Most Probable POS-Tag can improve accuracy in POS Tagging but does not always produce correct labels. Most Probable POS-Tag can remove zero-value probability from POS Tagging Hidden Markov Model. The results of this study indicate that POS Tagging using the Hidden Markov Model is influenced by the treatment of unknown words, vocabulary and word label relationships in the dataset.

Single Exponential Smoothing-Multilayer Perceptron Untuk Peramalan Pengunjung Unik Jurnal Elektronik Ferdinand, Miftakhul Anggita Bima; Wibawa, Aji Prasetya; Zaeni, Ilham Ari Elbaith; Rosyid, Harits Ar
Mobile and Forensics Vol 2, No 2 (2020)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v2i2.2034

Jumlah kunjungan rerata pengunjung unik per hari pada jurnal elektronik menunjukkan bahwa hasil terbitan karya ilmiah website tersebut menarik. Sehingga jumlah pengunjung unik dijadikan indikator penting dalam mengukur keberhasilan sebuah jurnal elektronik untuk memenuhi perluasan, penyebaran dan percepatan sistem akreditasi jurnal. Pengunjung Unik merupakan jumlah pengunjung per Internet Address (IP) yang mengakses sebuah jurnal elektronik dalam kurun waktu tertentu. Terdapat beberapa metode yang biasa digunakan untuk peramalan, diantaranya adalah Multilayer Perceptron (MLP).Â Â Kualitas data berpengaruh besar dalam membangun model MLP yang baik, karena sukses tidaknya permodelan pada MLP sangat dipengaruhi oleh data input. Salah satu cara untuk meningkatkan kualitas data adalah dengan melakukan smoothing pada data tersebut. Pada penelitian ini digunkan metode peramalan Multilayer Perceptron berdasarkan penelitian sebelumnya dengan kombinasi data training dan testing 80%-20% dengan asitektur 2-1-1 dan learning rate 0,4. Selanjutnya untuk meningkatkan kualitas data dilakukan smoothing dengan menerapkan metode Single Exponential Smoothing. Dari penelitian yang dilakukan diperoleh hasil terbaik menggunakan alpha 0.9 dengan hasil akurasi MSE 94.02% dan RMSE 75.54% dengan lama waktu eksekusi 580,27 detik.Â The number of visits by the average unique visitor per day on electronic journals shows that the published scientific papers on the website are interesting. So that the number of unique visitors is used as an important indicator in measuring the success of an electronic journal to meet the expansion, dissemination and acceleration of the journal accreditation system. Unique Visitors is the number of visitors per Internet Address (IP) who access an electronic journal within a certain period of time. There are several methods commonly used for forecasting, including the Multilayer Perceptron (MLP). Data quality has a big influence in building a good MLP model, because the success or failure of modeling in MLP is greatly influenced by the input data. One way to improve data quality is by smoothing the data. In this study, the Multilayer Perceptron forecasting method was used based on previous research with a combination of training data and testing 80% -20% with a 2-1-1 architecture and a learning rate of 0.4. Furthermore, to improve data quality, smoothing is done by applying the Single Exponential Smoothing method. From the research conducted, the best results were obtained using alpha 0.9 with MSE accuracy of 94.02% and RMSE 75.54% with a long execution time of 580.27 seconds.

Single Exponential Smoothing-Multilayer Perceptron Untuk Peramalan Pengunjung Unik Jurnal Elektronik Ferdinand, Miftakhul Anggita Bima; Wibawa, Aji Prasetya; Zaeni, Ilham Ari Elbaith; Rosyid, Harits Ar
Mobile and Forensics Vol. 2 No. 2 (2020)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v2i2.2034

Jumlah kunjungan rerata pengunjung unik per hari pada jurnal elektronik menunjukkan bahwa hasil terbitan karya ilmiah website tersebut menarik. Sehingga jumlah pengunjung unik dijadikan indikator penting dalam mengukur keberhasilan sebuah jurnal elektronik untuk memenuhi perluasan, penyebaran dan percepatan sistem akreditasi jurnal. Pengunjung Unik merupakan jumlah pengunjung per Internet Address (IP) yang mengakses sebuah jurnal elektronik dalam kurun waktu tertentu. Terdapat beberapa metode yang biasa digunakan untuk peramalan, diantaranya adalah Multilayer Perceptron (MLP). Kualitas data berpengaruh besar dalam membangun model MLP yang baik, karena sukses tidaknya permodelan pada MLP sangat dipengaruhi oleh data input. Salah satu cara untuk meningkatkan kualitas data adalah dengan melakukan smoothing pada data tersebut. Pada penelitian ini digunkan metode peramalan Multilayer Perceptron berdasarkan penelitian sebelumnya dengan kombinasi data training dan testing 80%-20% dengan asitektur 2-1-1 dan learning rate 0,4. Selanjutnya untuk meningkatkan kualitas data dilakukan smoothing dengan menerapkan metode Single Exponential Smoothing. Dari penelitian yang dilakukan diperoleh hasil terbaik menggunakan alpha 0.9 dengan hasil akurasi MSE 94.02% dan RMSE 75.54% dengan lama waktu eksekusi 580,27 detik. The number of visits by the average unique visitor per day on electronic journals shows that the published scientific papers on the website are interesting. So that the number of unique visitors is used as an important indicator in measuring the success of an electronic journal to meet the expansion, dissemination and acceleration of the journal accreditation system. Unique Visitors is the number of visitors per Internet Address (IP) who access an electronic journal within a certain period of time. There are several methods commonly used for forecasting, including the Multilayer Perceptron (MLP). Data quality has a big influence in building a good MLP model, because the success or failure of modeling in MLP is greatly influenced by the input data. One way to improve data quality is by smoothing the data. In this study, the Multilayer Perceptron forecasting method was used based on previous research with a combination of training data and testing 80% -20% with a 2-1-1 architecture and a learning rate of 0.4. Furthermore, to improve data quality, smoothing is done by applying the Single Exponential Smoothing method. From the research conducted, the best results were obtained using alpha 0.9 with MSE accuracy of 94.02% and RMSE 75.54% with a long execution time of 580.27 seconds.

Pelabelan Kelas Kata Bahasa Jawa Menggunakan Hidden Markov Model Mursyit, Mohammad; Wibawa, Aji Prasetya; Zaeni, Ilham Ari Elbaith; Rosyid, Harits Ar
Mobile and Forensics Vol. 2 No. 2 (2020)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/mf.v2i2.2450

Part of Speech Tagging atau POS Tagging adalah proses memberikan label pada setiap kata dalam sebuah kalimat secara otomatis. Penelitian ini menggunakan algoritma Hidden Markov Model (HMM) untuk proses POS Tagging. Perlakuan untuk unknown words menggunakan Most Probable POS-Tag. Dataset yang digunakan berupa 10 cerita pendek berbahasa Jawa terdiri dari 10.180 kata yang telah diberikan tagset Bahasa Jawa. Pada penelitian ini proses POS Tagging menggunakan dua skenario. Skenario pertama yaitu menggunakan algoritma Hidden Markov Model (HMM) tanpa menggunakan perlakuan untuk unknown words. Skenario yang kedua menggunakan HMM dan Most Probable POS-Tag untuk perlakuan unknown words. Hasil menunjukan skenario pertama menghasilkan akurasi sebesar 45.5% dan skenario kedua menghasilkan akurasi sebesar 70.78%. Most Probable POS-Tag dapat meningkatkan akurasi pada POS Tagging tetapi tidak selalu menunjukan hasil yang benar dalam pemberian label. Most Probable POS-Tag dapat menghilangkan probabilitas bernilai Nol dari POS Tagging Hidden Markov Model. Hasil penelitian ini menunjukan bahwa POS Tagging dengan menggunakan Hidden Markov Model dipengaruhi oleh perlakuan terhadap unknown words, perbendaharaan kata dan hubungan label kata pada dataset. Part of Speech Tagging or POS Tagging is the process of automatically giving labels to each word in a sentence. This study uses the Hidden Markov Model (HMM) algorithm for the POS Tagging process. Treatment for unknown words uses the Most Probable POS-Tag. The dataset used is in the form of 10 short stories in Javanese consisting of 10,180 words which have been given the Javanese tagset. In this study, the POS Tagging process uses two scenarios. The first scenario is using the Hidden Markov Model (HMM) algorithm without using treatment for unknown words. The second scenario uses HMM and Most Probable POS-Tag for treatment of unknown words. The results show that the first scenario produces an accuracy of 45.5% and the second scenario produces an accuracy of 70.78%. Most Probable POS-Tag can improve accuracy in POS Tagging but does not always produce correct labels. Most Probable POS-Tag can remove zero-value probability from POS Tagging Hidden Markov Model. The results of this study indicate that POS Tagging using the Hidden Markov Model is influenced by the treatment of unknown words, vocabulary and word label relationships in the dataset.

Exploration of genetic network programming with two-stage reinforcement learning for mobile robot Siti Sendari; Arif Nur Afandi; Ilham Ari Elbaith Zaeni; Yogi Dwi Mahandi; Kotaro Hirasawa; Hsien-I Lin
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 3: June 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i3.12232

This paper observes the exploration of Genetic Network Programming Two-Stage Reinforcement Learning for mobile robot navigation. The proposed method aims to observe its exploration when inexperienced environments used in the implementation. In order to deal with this situation, individuals are trained firstly in the training phase, that is, they learn the environment with ϵ-greedy policy and learning rate α parameters. Here, two cases are studied, i.e., case A for low exploration and case B for high exploration. In the implementation, the individuals implemented to get experience and learn a new environment on-line. Then, the performance of learning processes are observed due to the environmental changes.

Journal Classification Using Cosine Similarity Method on Title and Abstract with Frequency-Based Stopword Removal Piska Dwi Nurfadila; Aji Prasetya Wibawa; Ilham Ari Elbaith Zaeni; Andrew Nafalski
International Journal of Artificial Intelligence Research Vol 3, No 2 (2019): December 2019
Publisher : STMIK Dharma Wacana

Classification of economic journal articles has been done using the VSM (Vector Space Model) approach and the Cosine Similarity method. The results of previous studies are considered to be less optimal because Stopword Removal was carried out by using a dictionary of basic words (tuning). Therefore, the omitted words limited to only basic words. This study shows the improved performance accuracy of the Cosine Similarity method using frequency-based Stopword Removal. The reason is because the term with a certain frequency is assumed to be an insignificant word and will give less relevant results. Performance testing of the Cosine Similarity method that had been added to frequency-based Stopword Removal was done by using K-fold Cross Validation. The method performance produced accuracy value for 64.28%, precision for 64.76 %, and recall for 65.26%. The execution time after pre-processing was 0, 05033 second.

Perangkat Pengukuran Data Tumbuh Kembang dan Kesehatan pada Siswa TK Ilham Ari Elbaith Zaeni; I Made Wirawan; Muhammad Iqbal Akbar; Retno Indah Rokhmawati; Dessy Rif’a Anzani
TEKNO: Jurnal Teknologi Elektro dan Kejuruan Vol 32, No 1 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um034v32i1p175-184

Proses tumbuh kembang anak merupakan hal penting yang harus diperhatikan sejak dini untuk mencapai perkembangan yang optimal. Antropometri gizi berhubungan dengan berbagai macam pengukuran dimensi tubuh dan komposisi tubuh dari berbagai tingkat umur dan tingkat gizi. Ukuran antropometri terdiri dari berat badan dan tinggi badan. Tanda-tanda vital meliputi detak jantung, pernapasan (laju pernapasan), tekanan darah, dan suhu membantu menyadari masalah sejak dini atau menghilangkan kekhawatiran tentang keadaan anak. Penelitian ini bertujuan mengembangkan perangkat pengukuran data tumbuh kembang dan kesehatan pada siswa TK. Sistem Elektronik yang dikembangkan terdiri dari Mikrokontroler sebagai perangkat utama yang akan membaca input dari tombol setting dan beberapa sensor. Sensor yang dipasang terdiri dari sensor berat badan, sensor tinggi badan, sensor suhu badan, dan sensor denyut jantung serta oksimeter. Sensor untuk berat badan, tinggi badan, detak jantung, dan saturasi oksigen diuji selama tahap pengembangan. Data dari sensor akan ditransfer ke database melalui Wi-Fi menggunakan ESP8266. Informasi dalam database ini selanjutnya akan disimpan dan ditampilkan sebagai tabel dan grafik. Hasil pengujian pembacaan sensor menunjukkan bahwa pembacaan sensor mampu bekerja dengan baik. ESP8266 juga mampu mengirim data ke database. Data pembacaan sensor database dapat disajikan pada halaman website. Gadget pemantau data tumbuh kembang anak TK, serta kesehatannya, dapat berjalan dengan baik.

Opinion Analysis for Emotional Classification on Emoji Tweets using the Naïve Bayes Algorithm Siti Sendari; Ilham Ari Elbaith Zaeni; Dian Candra Lestari; Hanny Prasetya Hariyadi
Knowledge Engineering and Data Science Vol 3, No 1 (2020)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v3i12020p50-59

Opinion Analysis is a research study needed to social media, since the content could become a trending topic and has a significant impact on social life. One of the social media that have a big contribution to cyberspace and information development is Twitter. In the Twitter application, users can insert images that represent emotions, facial expressions, or icons. Emoji is a graphic symbol in the form of an image to express a thing, with the Emoji, a text can be read and understood according to its meaning because the image represents it. Of the several things that have been mentioned then, the researchers conducted research on the classification of tweet content based on the use of Emojis. This study aims to determine the emotional uses of Twitter in one period. Every tweet on the Twitter timeline, which contains both text and Emojis, will be classified according to several categories. The algorithm used was Naïve Bayes. It calculated the probability of Emoji tweet to obtain the text classification with Emojis. The results of the classification of emotions are grouped with three categories, namely "angry," "joy," and "sad," it showed that the category "joy" had become the emotional trend of Twitter users where Emojis (x1f60a) dominate the most. Meanwhile, the accuracy of the algorithm used to reach 90% with a 70:30 holdout technique.

Generating Javanese Stopwords List using K-means Clustering Algorithm Aji Prasetya Wibawa; Hidayah Kariima Fithri; Ilham Ari Elbaith Zaeni; Andrew Nafalski
Knowledge Engineering and Data Science Vol 3, No 2 (2020)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v3i22020p106-111

Stopword removal necessary in Information Retrieval. It can remove frequently appeared and general words to reduce memory storage. The algorithm eliminates each word that is precisely the same as the word in the stopword list. However, generating the list could be time-consuming. The words in a specific language and domain must be collected and validated by specialists. This research aims to develop a new way to generate a stop word list using the K-means Clustering method. The proposed approach groups words based on their frequency. The confusion matrix calculates the difference between the findings with a valid stopword list created by a Javanese linguist. The accuracy of the proposed method is 78.28% (K=7). The result shows that the generation of Javanese stopword lists using a clustering method is reliable.

Co-Authors A.N. Afandi Adam Rachmawan Adib Nur Sasongko Aditama Yudha Atmanegara Adjie Rosyidin Afifah Salim Afnan Habibi, M. Agung Bella Putra Utama Aji Prasetya Wibawa Aji Wibawa Akhmad Afrizal Rizqi Amalia Sufa Andrew Nafalski Andrew Nafalski Andy Hermawan Anggraeni Budiarti Anik N. Handayani Anik Nur Handayani Arengga Wibowo, Danang Arifin, Samsul Aripriharta - Arya Kusuma Wardhana Arya Tandy Hermawan Atmaja, Nimas Hadi Dessy Rif’a Anzani Dian Candra Lestari Dony Setiawan Dwiyanto, Felix Andika Dyah Lestari Eko Pambagyo Setyobudi Fanani, Erianto Faozan Fauzi, Rochmad Felix Andika Dwiyanto Felix Andika Dwiyanto Ferdiansyah, Dodik Septian Ferdinand, Miftakhul Anggita Bima Fitriana Kurniawati Gunawan Gunawan Gunawan Gwinny Tirza Rarastri Hanny Prasetya Hariyadi Hari Putranto Harits Ar Rosyid Hartono, Nickolas Hendrawan, William Hartanto Hidayah Kariima Fithri Hsien-I Lin I Made Wirawan Irvan, Mhd Ismail, Amelia Ritahani Ivatus Sunaifah Kartika Kirana Kevin Raihan Khafit Zaman Kotaro Hirasawa Liliek Rahayu M. Adib Nursasongko Maftuh Ahnan Mahisha Laila Moh. Iqbal Ardiansyah Mohamad Iqbal Mokh Sholihul Hadi Muhammad Arrazy Muhammad Firmansyah muhammad hafiizh, muhammad Muhammad Iqbal Akbar Muhammad Khusairi Osman Muhammad Rifai Muhammad Syauqi Muhammad Usman Mursyit, Mohammad Nafalski, Andrew Ningtyas, Yana Nusantar, Alrizal Akbar Nusantar Akbar Piska Dwi Nurfadila Prana Ihsanuddin, Adika Puji Santoso Pundhi Yuliawati Rasidy, Ahmad Himawari Retno Indah Rokhmawati Ridwan Shalahuddin Rina Dewi Indahsari Riris Andriani Rizal Kholif Nurrohman Ronny Afrian Samsul Arifin Setumin, Samsul Shandy Darmawan Simbolon, Triyanti Siti Sendari Sugiono, Bhima Satria Rizki Sujito Sujito Suyono Suyono Syaad Patmanthara Syafaat, Mokhammad Tri Atmadji Sutikno Utama, Agung Bella Putra Yandhika Surya Akbar Gumilang Yogi Dwi Mahandi Yosi Kristian Zafifatuz Zuhriyah

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