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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Proceeding of the Electrical Engineering Computer Science and Informatics Indonesian Journal on Software Engineering (IJSE) Faktor Exacta INOVTEK Polbeng - Seri Informatika Jurnal Sisfokom (Sistem Informasi dan Komputer) Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Jurnal ICT : Information Communication & Technology SENSITEK Respati SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Abdimas Sangkabira Kresna: Jurnal Riset dan Pengabdian Masyarakat Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) International Journal on Advanced Technology, Engineering, and Information System (IJATEIS) Eduvest - Journal of Universal Studies Telematika MKOM Jurnal Pengabdian Masyarakat Nasional Artinara Jurnal Ticom: Technology of Information and Communication
Mohammad Syafrullah
Universitas Budi Luhur
Aerospace Engineering Agriculture, Biological Sciences & Forestry 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 Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Neuroscience Social Sciences Other

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Penerapan Data Mining untuk Memprediksi Minat Nasabah Terhadap Produk Asuransi Meninggal Dunia dengan Metode Naïve Bayes (Studi Kasus : PT. BNI Life Insurance) Muhammad Syafrullah, Ari Hidayatullah, Ena Mudiawati,
Jurnal Teknologi Informasi RESPATI Vol 16, No 2 (2021)
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/jtir.v16i2.406

Abstract

INTISASIPendapatan untuk perusahaan asuransi ditentukan oleh jumlah premi yang dibayar oleh nasabah. faktor penting nasabah berupa premi, premi ditentukan dalam persentase atau tarif tertentu. Pada perusahaan asuransi pasti memiliki jumlah data, dan data tersebut sangat penting bagi perusahaan untuk mengetahui kriteria nasabah yang berminat pada asurnsi yang dipasarkan. Dengan adanya informasi dari  data  nasabah  yang  ada,  perusahaan  asuransi  dapat  mengambil  suatu keputusan dalam menerapkan stragi perusahaan diantarnya yaitu menjual produk- produk promo untuk meninggatkan pendapatan perusahaan. Data mining merupakan suatu teknologi yang dapat membantu perusahaan dalam menemukan suatu yang sangat penting dari sekumpulan data. Data mining dapat membentu sautu pola atau membuat suatu sifat perilaku bisnisa yang berguna untuk pengambilan keputusan. Dengan menggunakan metode algoritma Naive Bayes diharapkan bisa membantu perusahaan dalam pengelolaan data nasabah dengan cara mengklasifikasi data nasabah untuk memprediksi minat nasabah dengan tingkat akurasi melebihi 80% dalam memilih produk asuransi meninggal dunia. Kata Kunci: asuransi, baïve bayes, prediksi, data mining.   ABSTRACTIncome for insurance companies is determined by the amount of premium paid by the customer. Important factors for customers in the form of premiums, premiums are determined in certain percentages or rates. The insurance company certainly has the amount of data, and the data is very important for companies to know the criteria of customers who are interested in the insurance marketed. With the information from existing customer data, the insurance company can make a decision in implementing the company's strategy, which is to sell promo products to increase company revenue. Data mining is a technology that can help companies find a very important set of data. Data mining can form a pattern or create a nature of business behavior that is useful for decision making. By using the Naive Bayes algorithm method, it is expected to be able to assist companies in managing customer data by classifying customer data to predict customer interest with an accuracy rate exceeding 80% in choosing a death insurance product. Keywords: insurance, baïve bayes, predictions, data mining..
Penerapan Metode Haversine Pada Sistem Informasi Geografis Untuk Penentuan Lokasi Pembangunan Menara Telekomunikasi Pada Kota Tangerang Achmad Maulana; Achmad Solichin; Mohammad Syafrullah
Indonesian Journal on Software Engineering (IJSE) Vol 4, No 1 (2018): IJSE 2018
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (596.353 KB) | DOI: 10.31294/ijse.v4i1.6294

Abstract

Abstract - Development Telecommunication Tower growth cannot be avoided. Accordingly, the local government makes local regulations on Planning and Control of Telecommunication Tower to avoid illegal construction of Telecommunication Tower because makes a reduction in open land and also reduces the aesthetic value of the spatial region. The researcher conducted a study on the implementation of methods haversine on a geographic information system is expected to simplify the process of distance measurements for the determination of Telecommunication Tower towards he elliptical shape of the earth. Keywords: GIS, haversine, RAD, google map
Parameter Prediction for Lorenz Attractor by using Deep Neural Network Nurnajmin Qasrina Ann; Dwi Pebrianti; Mohammad Fadhil Abas; Luhur Bayuaji; Mohammad Syafrullah
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 8, No 3: September 2020
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v8i3.1272

Abstract

Nowadays, most modern deep learning models are based on artificial neural networks. This research presents Deep Neural Network to learn the database, which consists of high precision, a strange Lorenz attractor. Lorenz system is one of the simple chaotic systems, which is a nonlinear and characterized by an unstable dynamic behavior. The research aims to predict the parameter of a strange Lorenz attractor either yes or not. The primary method implemented in this paper is the Deep Neural Network by using Phyton Keras library. For the neural network, the different number of hidden layers are used to compare the accuracy of the system prediction. A set of data is used as the input of the neural network, while for the output part, the accuracy of prediction data is expected. As a result, the accuracy of the testing result shows that 100% correct prediction can be achieved when using the training data. Meanwhile, only 60% correct prediction is achieved for the new random data.
Pemantauan Suhu Pada Sistem Pemanas Air Menggunakan Temperatur Kontrol Dengan Metode Pid Ziegler Nichols Berbasis Web Agus Riyanto; Mohammad syafrullah
Proceeding Seminar Nasional Sistem Informasi dan Teknologi Informasi 2018: Proceeding Seminar Nasional Sistem Informasi dan Teknologi Informasi (SENSITEK)
Publisher : STMIK Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30700/pss.v1i1.392

Abstract

Banyak industri menggunakan pemanas untuk proses produksinya. Namun masalahnya adalah pemanas ini harus dikendalikan suhunya supaya suhu panasnya sesuai dengan yang diinginkan, karena jika tidak terkendali panasnya akan mengakibatkan overheating dan over pressure yang akan menyebabkan proses produksi menjadi gagal. Pada hasil kendali menggunakan metode on-off menujukan waktu ketika proses pemanasan air dari suhu awal 36,69  hingga nilai suhu yang diinginkan 73  yaitu 8 menit 14 detik sedangkan pada proses pemanasan menggunakan PID dari suhu awal 29.56   hingga nilai suhu yang diinginkan 73  yaitu 19 menit 14 detik. Nilai suhu dapat dilihat pada tampilan web baik dalam bentuk angka dengan satuan derajat celcius maupun dalam bentuk grafik. Sedangkan hasil pengujian alat terhadap penerimaan pengguna/responden menunjukan bahwa variabel PU (X1) dan variabel PEU (X2) terhadap penerimaan pengguna BITU (Y) dapat diterima. Hal ini ditunjukan dengan pengujian realibilitas dengan nilai croncbach’s alpa PU 0,759>0,60, PEU 0,669>0,60, validitas PU dan PEU menunjukan nilai r tabel lebih tinggi dari t tabel, uji regresi linear berganda PU (X1) dan PEU (X2) terhadap  penerimaan pengguna BITU (Y) menunjukkan nilai signifikan 0,000<0,05, model summary summary menujukan nilai (  )= 0.262 dan anova menujukan nilai Sig. F sebesar 0,000 < α = 0,05.Kata kunci: Sistem Kendali, PID, Node Mcu esp8266, suhu, Monitoring
Classification of Physiological Signals for Emotion Recognition using IoT Sadhana Tiwari; Sonali Agarwal; Muhammad Syafrullah; Krisna Adiyarta
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1943

Abstract

Emotion recognition gains huge popularity now a days. Physiological signals provides an appropriate way to detect human emotion with the help of IoT. In this paper, a novel system is proposed which is capable of determining the emotional status using physiological parameters, including design specification and software implementation of the system. This system may have a vivid use in medicine (especially for emotionally challenged people), smart home etc. Various Physiological parameters to be measured includes, heart rate (HR), galvanic skin response (GSR), skin temperature etc. To construct the proposed system the measured physiological parameters were feed to the neural networks which further classify the data in various emotional states, mainly in anger, happy, sad, joy. This work recognized the correlation between human emotions and change in physiological parameters with respect to their emotion.
Diagnosis of Smear-Negative Pulmonary Tuberculosis using Ensemble Method: A Preliminary Research Rusdah Rusdah; Mohammad Syafrullah
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1944

Abstract

Indonesia is one of 22 countries with the highest burden of Tuberculosis in the world. According to WHO’s 2015 report, Indonesia was estimated to have one million new tuberculosis (TB) cases per year. Unfortunately, only one-third of new TB cases are detected. Diagnosis of TB is difficult, especially in the case of smear-negative pulmonary tuberculosis (SNPT). The SNPT is diagnosed by TB trained doctors based on physical and laboratory examinations. This study is preliminary research that aims to determine the ensemble method with the highest level of accuracy in the diagnosis model of SNPT. This model is expected to be a reference in the development of the diagnosis of new pulmonary tuberculosis cases using input in the form of symptoms and physical examination in accordance with the guidelines for tuberculosis management in Indonesia. The proposed SNPT diagnosis model can be used as a cost-effective tool in conditions of limited resources. Data were obtained from medical records of tuberculosis patients from the Jakarta Respiratory Center. The results show that the Random Forest has the best accuracy, which is 90.59%, then Adaboost of 90.54% and Bagging of 86.91%.
Testing Big Data Applications Narinder Punn; Sonali Agarwal; Muhammad Syafrullah; Krisna Adiyarta
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1952

Abstract

Today big data has become the basis of discussion for the organizations. The big task associated with big data stream is coping with its various challenges and performing the appropriate testing for the optimal analysis of the data which may benefit the processing of various activities, especially from a business perspective. Big data term follows the massive volume of data, (might be in units of petabytes or exabytes) exceeding the processing and analytical capacity of the conventional systems and thereby raising the need for analyzing and testing the big data before applications can be put into use. Testing such huge data coming from the various number of sources like the internet, smartphones, audios, videos, media, etc. is a challenge itself. The most favourable solution to test big data follows the automated/programmed approach. This paper outlines the big data characteristics, and various challenges associated with it followed by the approach, strategy, and proposed framework for testing big data applications.
Prediction Of Students Academic Success Using Case Based Reasoning Abdul Rahman; Rezza Anugrah Mutiarawan; Agung Darmawan; Yan Rianto; Mohammad Syafrullah
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1956

Abstract

Academic success for a student is influenced by many factors during their study period. Factors such as student gender, student absenteeism, parental satisfaction with schools, relations and parents who are responsible for students can influence student success in the academic field. Researchers try to find out what are the most dominant factors in determining academic success for a student at different levels of education such as elementary, middle and high school level. Previous research grouped the level of student academic success into three levels, namely low, medium, high and obtained 15 Association Rules Generated By Apriori Algorithm. This study tried to find out and predict the possible level of academic success of students by using 9 Association Rules Generated By Apriori Algorithm from previous research. The method used to predict the level of student academic success is case based reasoning with the nearest neighbor algorithm. By using the Association Rules Generated By Image Algorithm and with the data set from the xAPIEducational Mining Dataset the case similarity value was obtained with knowledge data that is 1 with a percentage of 81%, and data that had a similarity value of less than 1 was 19%. While in the previous study the best classification accuracy was 80.6% by the Voting classifier. And the grouping of success data is divided into two, namely low and high.
Fish Eggs Calculation Models Using Morphological Operation Syaipul Ramdhan; Muhammad Syafrullah
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1959

Abstract

Calculations on group objects are the concern of current researchers, to find optimal detection and calculation solutions. One of them is fish eggs in a group. Fish cultivators need precision in calculations, because currently conventional methods often make errors in calculations. If the calculation is wrong, it will have an impact on production and sales that are not balanced (loss). Small and easily broken fish eggs are grouped and it isdifficult to do manual calculations. The purpose of this study is to test which segmentation method is the most optimal in calculating these grouped fish egg objects and produce precise and fast calculations. The test model was developed from algorithm of morphological operations,watershed and statistical approaches with the same number of samples. The result shows morphological operation is better than the others with 96.67%, watershed 81.28% and the count statistic is 95.62% with an average calculation process speed of 54.5 seconds for morphological operations, watershed 1 minute 55 seconds and statistical approach 58.9 seconds. As a result. morphology gets the most optimal and fast calculation results.
A Third Order based Additional Regularization in Intrinsic Space of the Manifold Rakesh Kumar Yadav; Abhishek Singh; Shekhar Verma; S. Venkatesan; M. Syafrullah; Krisna Adiyarta
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1961

Abstract

Second order graph Laplacian regularization has the limitation that the solution remains biased towards a constant which restricts its extrapolationcapability. The lack of extrapolation results in poor generalization. An additional penalty factor is needed on the function to avoid its over-fitting on seen unlabeled training instances. The third order derivative based technique identifies the sharp variations in the function and accurately penalizes them to avoid overfitting. The resultant function leads to a more accurate and generic model that exploits the twist and curvature variations on the manifold. Extensive experiments on synthetic and real-world data set clearly shows thatthe additional regularization increases accuracy and generic nature of model.
Co-Authors Abdul Rahman Abdul Rahman Wahid Abhishek Abhishek Abhishek Singh Abhishek, Abhishek Achmad Maulana Achmad Solichin Adiyarta, Krisna Agarwal, Prachi Agarwal, Sonali Agarwal, Sonali Agarwal, Sonali Agung Darmawan Agus Riyanto Andrico Andrico Aria Mustofa Hidayat Armando Ondihon Kristoper Purba Arumgam, Yogesvaran Bayuaji, Luhur Darmawan, Agung Devit Setiono Dewi, Ernawati Dhannuri, Syam Prasad Dwi Pebranti Dwi Pebrianti Elizabeth Yohanes Emil Salim Ernawati Dewi Esti Setiasih Gaol, GA Monang Lumban Hadi Syahrial Hanif, Raihan Labib Indra Riyanto Irawan Irawan Jamhari Jamhari K Singh Kalyzta, Juan Kassim, Siti Rafidah Binti Krisna Adiyarta Kusumaningsih, Dewi Luhur Bayuaji M. Ivan Putra Eriansya Makhdum Rosadi Mardi Hadjianto Martono Martono Maulidia, Mia Meilieta Anggriani Porrie Mohammad Fadhil Abas Muhammad Arya Java Muhammad Azhar Mujahid Muhammad Hasanul Huda Muhammad Sumarudin Mutiarawan, Rezza Anugrah Nagabhushan, P. Narinder Punn Nugraha Abdullah, Indra Nurnajmin Qasrina Ann Nurnajmin Qasrina Ann Ayop P. Nagabhushan Painem, Painem Pandu Pradinata Pebranti, Dwi Porrie, Meilieta Anggriani Prachi Agarwal Prasetiamaolana, Eko Pudoli, Ahmad Punn, Narinder Purba, Armando Ondihon Kristoper Purwanto Purwanto Purwanto Purwanto Qasrina Ann Ayop, Nurnajmin Rakesh Kumar Yadav Ramdhan, Syaipul Ratna Kusumawardani Ratna Kusumawardani, Ratna Rezza Anugrah Mutiarawan Rianto, Yan Ridho Saputra Rizki Aji Wibowo Roeswidiah, Ririt Rusdah Rusdah Ruwirohi, Jan Everhard S. Venkatesan Sadhana Tiwari Sambhavi Tiwari Samidi Samidi Sanjay Kumar Sonbhadra Sanjay Kumar Sonbhadra Sari, Widya Kumala Setyawan Widyartoh Shekhar Verma Shkehar Verma Singh, Abhishek Singh, K Siti Rafidah Binti Kassim Sonali Agarwal Sonali Agarwal Sonali Agarwal Sonbhadra, Sanjay Kumar Sonbhadra, Sanjay Kumar Supardi Supardi Supardi Supardi Supardi, Supardi Syaddad, Muhammad Sulthan Syaiful Anwar Syaipul Ramdhan Syam Prasad Dhannuri Thisa Tri Utami Tiwari, Sadhana Tiwari, Sambhavi Triana Anggraini Tutik Sri Susilowati Venkatesan, S. Verma, Shekhar Verma, Shkehar Victor Ilyas Sugara Widya Kumala Sari Widyartoh, Setyawan Windarto Windarto Windarto, Windarto Yadav, Rakesh Kumar Yan Rianto Yodi Susanto Yogesvaran Arumgam Yulianawati Yulianawati Yulianawati Yulianawati Yulianawati Zulkarnaen Noor Syarif