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All Journal Jurnal Informatika dan Teknik Elektro Terapan Jurnas Nasional Teknologi dan Sistem Informasi Journal of Information Technology and Computer Science Jurnal Informatika JURNAL ILMIAH INFORMATIKA JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Jurnal Mnemonic JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Pengabdian Masyarakat Sains dan Teknologi Journal of Empowerment and Community Service Aspirasi : Publikasi Hasil Pengabdian dan Kegiatan Masyarakat Journal of Innovative and Creativity JSE Journal of Science and Engineering
Fendy Yulianto
Universitas Muhammadiyah Kalimantan Timur
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Transportation Other

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ANALISIS PENYAKIT PADA TUMBUHAN HIDROPONIK SELADA MENGGUNAKAN METODE FORWARD CHAINING Khoirul Huda Dwi Putra Huda; Arbansyah Arbansyah; Fendy Yulianto
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 2 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i2.14957

Abstract

This research, titled "Analysis of Diseases in Hydroponic Lettuce Plants Using the Forward Chaining Method," focuses on the process of identifying diseases in hydroponic lettuce plants through an expert system. Hydroponic lettuce plants can be affected by various diseases such as soft root, root rot, yellowing leaves, and others. Therefore, there is a need to facilitate farmers and laypeople in detecting diseases in hydroponic lettuce plants and easily identifying them by simply answering diagnostic questions about the disease symptoms. This research develops the results of the disease analysis in hydroponic lettuce plants using the Forward Chaining method through an expert system. The Forward Chaining method is used due to its high effectiveness and accuracy in identifying diseases through IF-THEN Rule s by finding facts from the established Rule s. The data presented includes disease data and symptom data obtained from hydroponic lettuce cultivation on Jalan Muang RT 47 Lempake. This research involves data collection, data analysis, and BlackBox testing. The development of the website for analyzing diseases in hydroponic lettuce plants using the Forward Chaining method employs PHP, HTML, CSS, and MySQL programming languages. The results of this research are satisfactory because the Forward Chaining method can accurately detect diseases, and the website runs smoothly and also got an accuracy of 79,16% on the calculation system using the website.
PERBANDINGAN METODE K–NEAREST NEIGHBOR (KNN) DAN NAIVE BAYES TERHADAP ANALISIS SENTIMEN PADA PENGGUNA E-WALLET APLIKASI DANA MENGGUNAKAN FITUR EKSTRAKSI TF-IDF Muhammad Rayhan Elfansyah Rayhan; Rudiman Rudiman; Fendy Yulianto Fendy
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 2 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i2.15009

Abstract

This research compares the accuracy of the K-Nearest Neighbor (KNN) and Naive Bayes methods in classifying user sentiment towards the DANA e-wallet application using Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction. User review data was collected through web scraping techniques and labeled by linguists and lexicon models. After undergoing pre-processing steps such as case folding, cleaning, tokenizing, stopword removal, and stemming, the data was classified using the KNN and Naive Bayes methods. The research results indicate that data labeling by linguists significantly improves the accuracy of both classification methods. Additionally, using TF-IDF as a word weighting method proves effective in enhancing the performance of sentiment classification models. Sentiment analysis of user reviews of the DANA application reveals various complaints and issues faced by users, providing information that can be used to improve the features and services offered, thereby increasing user satisfaction. This research also provides a comparison between the KNN and Naive Bayes methods, which can serve as a reference for other researchers in selecting appropriate methods for sentiment analysis on similar datasets.
ANALISIS SENTIMEN PADA ULASAN APLIKASI GOOGLE MAPS TERHADAP PELAYANAN BADAN PENYELENGGARA JAMINAN SOSIAL (BPJS) KESEHATAN SAMARINDA MENGGUNAKAN METODE K-NEAREST NEIGHBOR DENGAN FITUR EKSTRAKSI TF-IDF Ikhsan Nuttakwa Takbirata Ihram Nabawi Ikhsan; Rudiman Rudiman; Fendy Yulianto Fendy
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 2 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i2.15010

Abstract

This study aims to analyze public sentiment towards the services of BPJS Kesehatan Samarinda based on reviews on the Google Maps application. The method used in this research is K-Nearest Neighbor (KNN) with TF-IDF (Term Frequency-Inverse Document Frequency) feature extraction. The data used consists of 500 Indonesian-language reviews collected through web scraping techniques. After the data collection process, the data was labeled by an expert, and then a pre-processing stage was carried out, including case folding, cleaning, tokenizing, stop word removal, and stemming. The data was then weighted using the TF-IDF method to identify important words. The testing was conducted using a training and testing data ratio of 70:30 and a k value of 5. The results showed that the KNN method was able to classify positive and negative sentiments with an accuracy rate of 93.3%. This analysis provides an overview of the service quality of BPJS Kesehatan in Samarinda and can be used as a basis for service improvements. Additionally, this research contributes to the use of KNN and TF-IDF for sentiment analysis, opening opportunities for further research in this field.
Manajemen Bandwidth Pada Kantor Utama Distrik Navigasi Kelas 1 Menggunakan Mikrotik Arif Ramadhani; Muhammad Taufiq Sumadi; Fendy Yulianto
Jurnal Pengabdian Masyarakat Sains dan Teknologi Vol. 2 No. 4 (2023): Desember : Jurnal Pengabdian Masyarakat Sains dan Teknologi
Publisher : Fakultas Teknik Universitas Cenderawasih

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58169/jpmsaintek.v2i4.287

Abstract

This material discusses the implementation of bandwidth management using Queue Tree at the Samarinda Class 1 Navigation District Head Office. The internet is identified as a basic need in the operations of this government agency, especially those related to shipping safety. This activity aims to improve work efficiency and user experience through proper bandwidth management. The solution to the problem proposed is to carry out network management at the main office of the Samarinda Class 1 Navigation District using Mikrotik. The method used has 3 stages, namely design, implementation and trial. - respectively, then at the implementation stage the procedures for implementing bandwidth management will be explained step by step. The implementation results are validated through trials using a speed tester.The results of testing and design were compared, and it was found that the average difference for downloading was 1.945 while for uploading it was 1.55.
Prediksi Kurs Mata Uang Rupiah Terhadap Ringgit Malaysia Menggunakan Algoritma Backpropagation Tirta, Muhamad Wahyu; Nursyarif, Muhammad Khumaidi; Hasmadi, Ipan; Akbar, Farhan; Yulianto, Fendy
Jurnal Informatika Vol 11, No 1 (2024): April 2024
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v11i1.20946

Abstract

Nilai tukar mata uang di era globalisasi memegang peran sentral dalam stabilitas ekonomi suatu negara. Diperlukan sebuah analisis pergerakan terhadap nilai tukar agar bisa mengantisipasi terjadinya lonjakan terhadap fluktuasi nilai tukar. Sehingga muncul tantangan baru dalam melakukan fluktuasi kurs mata uang Rupiah terhadap ringgit Malaysia. Dataset yang digunakan adalah Data Kurs mata uang Ringgit Malaysia ke Rupiah periode 1 Juli - 30 Oktober 2023 dengan total data sebanyak 109. Penelitian ini berfokus pada metode Backpropagation dalam meningkatkan akurasi prediksi. Hasil penelitian menggunakan Epoch 300, Neuron 3, dan Learning Rate 0,5 menghasilkan nilai RMSE pada pelatihan Data Training: 13,601 dan Data Testing: 10,721 hal ini menandakan bahwa model mampu memberikan prediksi yang akurat dan mampu menggeneralisasi dengan baik terhadap data yang belum pernah dilihat sebelumnya. Secara keseluruhan, pengembangan model prediksi menggunakan Algoritma Backpropagation ini dapat dianggap berhasil, dan model ini mempunyai potensi untuk menjadi alat yang bermanfaat dalam pengambilan keputusan terkait prediksi nilai tukar mata uang dalam konteks pasar keuangan. Currency exchange rates in the era of globalization play a central role in the economic stability of a country. An analysis of exchange rate movements is needed in order to anticipate spikes in exchange rate fluctuations. So new challenges arise in fluctuating the Rupiah exchange rate against the Malaysian ringgit. The dataset used is Malaysian Ringgit to Rupiah currency exchange data for the period 1 July - 30 October 2023 with a total of 109 data. This research focuses on the Backpropagation method in increasing prediction accuracy. The results of the research using Epoch 300, Neuron 3, and Learning Rate 0.5 produced an RMSE value for Training Data Training: 13.601 and Testing Data: 10.721. This indicates that the model is able to provide accurate predictions and is able to generalize well to data that has never been seen. previously. Overall, the development of a prediction model using the Backpropagation Algorithm can be considered successful, and this model has the potential to become a useful tool in making decisions regarding currency exchange rate predictions in the context of financial markets. 
Pengukuran Dan Pemetaan Fotogrametris Menggunakan Unmaned Aerial Vehicle (UAV) Di Kota Samarinda Kelurahan Sungai Pinang Dalam Wahyu Adiwinata; Muhammad Taufiq Sumadi; Fendy Yulianto
ASPIRASI : Publikasi Hasil Pengabdian dan Kegiatan Masyarakat Vol. 2 No. 1 (2024): Januari : Publikasi Hasil Pengabdian dan Kegiatan Masyarakat
Publisher : Asosiasi Periset Bahasa Sastra Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/aspirasi.v2i1.191

Abstract

Soil plays an important role for all living things, where soil is a source of livelihood. It acts as a planting medium, a water reservoir, and a place for us to do activities. All of these activities are carried out on the land (Natasha Hutabarat et al., n.d.). In the community itself, land ownership is always a problem that is often encountered, whether it is the problem of unfixed land boundaries, land tenure, and land grabbing. The National Land Agency (BPN) as the institution that organizes land registration has a Complete Systematic Land Registration (PTSL) program that makes it easier for the community to register land so as to avoid land conflicts. In order to accelerate land registration, PTSL in Samarinda City this time uses media or tools to take measurements using the photogrammetric method, namely the Unmanned Aerial Vehicle (UAV) to obtain aerial images or the latest images. In its implementation, the community is asked to install boundary signs that have been delivered or socialized by BPN employees. So that the boundaries of the land plot can be seen from the UAV and visible from aerial images
IMPLEMENTASI METODE NAIVE BAYES UNTUK KLASIFIKASI KECELAKAAN LALU LINTAS DI KOTA SAMARINDA Salsabila, Cindy Azra; Yulianto, Fendy; Siswa, Taghfirul Azhima Yoga
Jurnal Informatika dan Teknik Elektro Terapan Vol 13, No 1 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i1.5890

Abstract

Kecelakaan lalu lintas merupakan permasalahan serius di Kota Samarinda yang dipengaruhi oleh berbagai faktor seperti kondisi cahaya, cuaca, kelas jalan, tipe jalan, kondisi permukaan jalan, kemiringan jalan, batas kecepatan di lokasi, dan status jalan berkontribusi terhadap tingkat kecelakaan lalu lintas. Dalam mengatasi permasalahan penentuan kecelakaan lalu lintas dapat menggunakan konsep klasifikasi dengan metode Naive Bayes. Data yang digunakan akan dibagi menjadi dua bagian dengan rasio 80:20 untuk pelatihan dan pengujian, serta divalidasi menggunakan K-Fold Cross Validation dengan K=12, kemudian didapatkan hasil akurasi sebesar 84%. Hasil ini menunjukkan bahwa metode Naive Bayes dapat digunakan untuk melakukan penentuan jenis kecelakaan lalu lintas yang ada di Kota Samarinda.
Penerapan Metode Analytical Hierarchy Process dan Simple Additive Weighting Dalam Penentukan Lokasi Pembuatan Rumah Burung Walet Nur, Seftiani; Yulianto, Fendy; Rahim, Abdul
Jurnal Nasional Teknologi dan Sistem Informasi Vol 10 No 3 (2024): Desember 2024
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v10i3.2024.182-191

Abstract

Burung walet adalah penghuni khas wilayah tropis dan lembab, yang hidup berkelompok dan membangun sarang dari air liur di gua atau tempat lembab dan gelap. Sarang burung walet memiliki banyak manfaat kesehatan, termasuk mempercepat regenerasi sel, memperkuat sistem kekebalan tubuh, dan menjaga Kesehatan pencernaan, sehingga memiliki nilai jual yang tinggi. Oleh karena itu, budidaya sarang burung walet memerlukan perencanaan yang cermat terkait penentuan lokasi yang tepat untuk memaksimalkan hasil. Penentuan lokasi yang tidak tepat dapat menyebabkan kerugian akibat Gedung walet yang tidak ditempati. Untuk membantu petani walet dalam menentukan lokasi yang tepat, berbagai metode, termasuk pengamatan langsung dan penggunaan aplikasi sistem cerdas dapat digunakan. Sistem cerdas seperti Sistem Pendukung Keputusan (SPK) dapat memberikan rekomendasi menggunakan metode AHP-SAW. Metode AHP memberikan kontribusi dalam pengambilan keputusan dengan mempertimbangakan kriteria yang telah ditentukan, sementara metode SAW digunakan untuk proses perangkingan. Hasil pengujian metode AHP menunjukan akurasi sebesar 63%, sementara kombinasi metode AHP-SAW menunjukan akurasi sebesar 73%. Dengan demikian, kombinasi metode AHP-SAW diharapkan dapat memberikan rekomendasi lokasi yang lebih tepat untuk pembuatan rumah burung walet, sehingga meningkatkan efisien dan hasil budidaya.
ANALISIS KEPUASAN PELANGGAN MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBORS PADA PERUSAHAAN UMUM DAERAH AIR MINUM BATIWAKKAL BERAU Rahmadana, Novia; Rahim, Abdul; Yulianto, Fendy
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 9 No 2 (2024): OCTOBER
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v9i2.51236

Abstract

Kepuasan pelanggan terhadap layanan air bersih merupakan aspek penting yang perlu dianalisis oleh Perusahaan Umum Daerah Air Minum Batiwakkal Berau untuk memastikan pelayanan terbaik. Penelitian ini bertujuan mengimplementasikan algoritma K-Nearest Neighbors (KNN) untuk menganalisis data kepuasan pelanggan Air Minum Battiwakkal Berau. Penelitian ini mengevaluasi akurasi model KNN dalam mengklasifikasikan tingkat kepuasan pelanggan, menentukan nilai terbaik parameter K, dan menyediakan gambaran mengenai persepsi pelanggan terhadap layanan Perusahaan Umum Daerah Air Minum Batiwakkal Berau. Metode penelitian melibatkan pengumpulan data dari pelanggan Air Minum Batiwakkal Berau. Data dipisahkan menjadi set pelatihan dan pengujian, diikuti dengan proses normalisasi. Model KNN dilatih dengan berbagai nilai K untuk menemukan parameter terbaik. Hasil penelitian menunjukkan bahwa algoritma KNN dapat mengklasifikasikan tingkat kepuasan pelanggan dengan akurasi memadai. Nilai terbaik K yang ditemukan adalah 14, memberikan performa terbaik dalam prediksi kepuasan pelanggan dengan nilai akurasi sebesar 85.96%.
Comparison of Regression, Support Vector Regression (SVR), and SVR-Particle Swarm Optimization (PSO) for Rainfall Forecasting Yulianto, Fendy; Mahmudy, Wayan Firdaus; Soebroto, Arief Andy
Journal of Information Technology and Computer Science Vol. 5 No. 3: Desember 2020
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1148.218 KB) | DOI: 10.25126/jitecs.20205374

Abstract

Rainfall is one of the factors that influence climate change in an area and is very difficult to predict, while rainfall information is very important for the community. Forecasting can be done using existing historical data with the help of mathematical computing in modeling. The Support Vector Regression (SVR) method is one method that can be used to predict non-linear rainfall data using a regression function. In calculations using the regression function, choosing the right SVR parameters is needed to produce forecasting with high accuracy. Particle Swarm Optimization (PSO) method is one method that can be used to optimize the parameters of the existing SVR method, so that it will produce SVR parameter values with high accuracy. Forecasting with rainfall data in Poncokusumo region using SVR-PSO has a performance evaluation value that refers to the value of Root Mean Square Error (RMSE). There are several Kernels that will be used in predicting rainfall using Regression, SVR, and SVR-PSO with Linear Kernels, Gaussian RBF Kernels, ANOVA RBF Kernels. The results of the performance evaluation values obtained by referring to the RMSE value for Regression is 56,098, SVR is 88,426, SVR-PSO method with Linear Kernel is 7.998, SVR-PSO method with Gaussian RBF Kernel is 27.172, and SVR-PSO method with ANOVA RBF Kernel is 2.193. Based on research that has been done, ANOVA RBF Kernel is a good Kernel on the SVR-PSO method for use in rainfall forecasting, because it has the best forecasting accuracy with the smallest RMSE value.
Co-Authors Abdul Rahim Akbar, Farhan Anitasari, Dini Arbansyah Arbansyah Arbansyah Arief Andy Soebroto Arif Ramadhani Atiqah, Nur Quratul Nabila Betris Dea Maretta, Nanda Hasmadi, Ipan Ikhsan Nuttakwa Takbirata Ihram Nabawi Ikhsan Ilham, Muhammad Fauzan Nur Khoirul Huda Dwi Putra Huda Muhammad Rayhan Elfansyah Rayhan Muhammad Taufiq Sumadi Muthmainnah Muthmainnah Muthmainnah Muthmainnah Naufal Azmi Verdikha Nur, Seftiani Nurdin, Andi Nursyarif, Muhammad Khumaidi Rahmadana, Novia Ridhoi, Reno Rudiman Rudiman Salsabila, Cindy Azra Sipahutar, Enriko Chiesa Tagfirul Azima Yoga Siswa, Muspidar Mulyadi Tirta, Muhamad Wahyu Umar Bakri Wahyu Adiwinata Wayan Firdaus Mahmudy Yoga, Taghfirul Azhima