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All Journal Jurnal Ilmiah Informatika Komputer Jurnal Simetris Jurnal Buana Informatika Journal of Telematics and Informatics Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Scientific Journal of Informatics KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Format : Jurnal Imiah Teknik Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika JRSI (Jurnal Rekayasa Sistem dan Industri) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Informatika Universitas Pamulang Jurnal SITECH : Sistem Informasi dan Teknologi JATI (Jurnal Mahasiswa Teknik Informatika) Joined Journal (Journal of Informatics Education Aiti: Jurnal Teknologi Informasi Journal of Computer Networks, Architecture and High Performance Computing E-JOINT (Electronica and Electrical Journal Of Innovation Technology) Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Jurnal Pengabdian Masyarakat untuk Negeri (UN-PENMAS) Ilmu Komputer untuk Masyarakat Jurnal Ilmiah Teknik Informatika & Elektro (JITEK) Scientific Journal of Informatics Jurnal Pengabdian Siliwangi
Rianto, Rianto
Departement Of Informatics, Universitas Siliwangi
Agriculture, Biological Sciences & Forestry Humanities 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Sosialisasi dan Pelatihan Penggunaan Aplikasi SMART Guna Mendukung Tertib Administrasi di Lingkungan Rukun Tetangga Rahmatulloh, Alam; Rianto, Rianto; Gunawan, Rohmat; Rizal, Randi
UN PENMAS (Jurnal Pengabdian Masyarakat untuk Negeri) Vol 4 No 1 (2024): UN PENMAS Vol 4 No 1
Publisher : LPPM Universitas Narotama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29138/un-penmas.v4i1.2562

Abstract

Rukun Tetangga (RT) is a community organization formed through deliberation and consensus of the local community to maintain harmony in life. Managing population administration at the RT level is one of the responsibilities of the RT Head. Population administration needs to be managed well, because population data is a basic statistical source in making various policies. In order to create administrative order in managing population data, in this service activity, socialization and training on the use of the SMART application (Sistem Informasi Manajemen Administrasi Rukun Tetangga) is carried out. Residents of RT 04 RW 07 Arjasari Village, Leuwisari District, Tasikmalaya Regency are partners in this service activity. There are several stages carried out in this service activity, including: stage 1 initial preparation, stage 2 main program, stage 3 closing the activity. Socialization and training on using the SMART application was carried out at partner locations on Friday 15 September. This application is expected to facilitate and make it easier for RT Heads and residents to complete population data and support orderly administration.
Penerapan Naïve Bayes untuk Prediksi Customer Churn (Studi Kasus: PT Hutchison 3 Indonesia) Alfarez, Rifky; Rianto, Rianto; Purwayoga, Vega
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol 5, No 02 (2024): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/jrami.v5i2.8556

Abstract

Penelitian ini memiliki maksud untuk mengembangkan model prediksi dengan memanfaatkan metode Naive Bayes. Pemodelan algoritma Naive Bayes dilakukan dengan menerapkan penggunaan Jupyter Notebook dan mengacu pada dataset yang terdiri dari atribut durasi langganan, frekuensi transaksi, tingkat kepuasan, dan status churn. Pada tahap awal, dilakukan eksplorasi data untuk memahami distribusi dan karakteristik atribut. Kemudian, dilakukan pengolahan data dengan menghapus kolom yang tidak relevan, memisahkan dataset menjadi data pelatihan dan data pengujian. Berikutnya, dilakukan pemodelan Naive Bayes dengan menghitung probabilitas kemunculan setiap nilai atribut untuk kelas churn true dan false. Probabilitas ini digunakan dalam perhitungan prediksi kelas churn berdasarkan atribut yang diberikan. Setelah model Naive Bayes terbentuk, dilakukan evaluasi performa model menggunakan metrik evaluasi. Evaluasi dilakukan dengan membandingkan kelas prediksi dengan kelas aktual pada data pengujian. Evaluasi menunjukkan model Naive Bayes menghasilkan akurasi sebesar 91,3%. Presisi, recall, dan F1-score sebesar 95% menunjukkan kemampuan model dalam mengklasifikasikan data churn dengan tingkat keakuratan yang tinggi
ANALISA USABILITY DESAIN USER INTERFACE MENGGUNAKAN METODE HEURISTICS EVALUATION DAN IMPORTANT PERFORMANCE ANALYSIS (IPA): STUDI KASUS : WEBSITE SUPER INFORMATIKA UNIVERSITAS SILIWANGI Fauzi Athallah, Zico; Rianto, Rianto; Muhammad Adi Khairul A
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 7 No. 1 (2023): JATI Vol. 7 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v7i1.5790

Abstract

Website di era globalisasi ada hal yang sudah sangat lumrah ditemukan dimana-mana. Namun pada kenyataannya penggunaan website pada kehidupan sehari-hari dinilai masih belum maksimal penggunaannya. Salah satu penyebab kurangnya pemanfaatan user interface pada website. Interface atau antarmuka yang berfungsi untuk menjembatani antara pengguna dengan teknologi itu sendiri. Terdapat banyak cara atau metode yang dapat digunakan untuk mengevaluasi usability desain user interface sebuah website salah satunya adalah metode Heuristic. Important Performance Analysis atau biasa disebut dengan IPA memiliki fungsi untuk mengukur aspek-aspek yang di anggap paling berguna bagi pengguna. Dengan menggabungkan metode heuristics dan metode IPA pada data yang telah dilakukan pengujian validitas dan reliabilitas, di dapatkan hasil analisis untuk menilai pemanfaatan usability desain user interface pada website berdasarkan nilai heuristic pada kepentingan dan performa menurut pengguna yang menghasilkan informasi berupa letak ke 20 indikator pada 4 kuadran kartesius dan 4 diantaranya masuk kedalam prioritas utama dalam perbaikan atau pengembangan, dimana tingkat kepuasan pengguna tidak mencerminkan prioritas utama dalam perbaikan seperti yang terlihat pada kuadran b terdapat indikator yang memiliki tingkat kepuasan yang lebih rendah dari pada indikator di kuadran a.
Workshop dan Pemanfaatan Teknologi Digital Signature berbasis Blockchain di Lingkungan Pimpinan Daerah Muhammadiyah Tasikmalaya Rahmatulloh, Alam; Rianto, Rianto; Gunawan, Rohmat; Rizal, Randi; Nugraha, Cindera Syaiful; Purwayoga, Vega
Ilmu Komputer untuk Masyarakat Vol 5, No 2 (2024)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkomas.v5i2.2443

Abstract

Dalam suatu organisasi, validasi dokumen berupa tanda tangan, merupakan salah aktifitas yang umum dilakukan. Proses pembubuhan tanda tangan dokumen secara konvensional memerlukan waktu dan tidak dapat dilakukan jika orang yang menandatangan dokumen tidak ada di tempat. Agar proses penandatanganan dokumen dapat dilakukan dengan mudah, dalam kegiatan pengabdian ini dilakukan workshop dan pemanfaatan teknologi digital signature berbasis blockchain yang diimplementaasikan pada aplikasi SignMu. Tahapan kegiatan pengabdian yang dilakukan diantaranya: persiapan awal, pelaksanaan, evaluasi dan pelaporan. Pada tahap persiapan  awal, dilakukan identifikasi msalah terkait proses penandatangan dokumen yang dilakukan di lokasi mitra. Sosialisasi dan pelatihan penggunaan aplikasi merupakan aktifias utama yang dilakukan pada  tahap pelaksanaan. Pada tahap akhir dilakukan evaluasi terhadap pelaksanaan kegiatan dan penyusunan laporan hasil kegiatan. Kegiatan pengabdian dilaksanakan pada hari Jum’at, 28 Juni 2024, pukul 13:30, bertempat di Kantor Pimpinan Daerah Muhammdiyag (PDM) Kabupaten Tasikmalaya, yang beralamat di Jalan Kalawagar, Singasari, Kecamatan Singaparna, Kabupaten Tasikmalaya Jawa Barat. Kegiatan pengabdian masyarakat ini diikuti oleh 13 peserta terdri dari: pengurus PDM Kabupaten Tasikmalaya dan tim pelaksana pengabdian dari Universitas Siliwangi. Hasil evaluasi terhadap kegiatan pengabdian ini, berdasarkan jawaban kuisioner oleh responden yang berasal dari mitra, rata-rata kategori “Sangat Setuju”=54.55%, “Setuju”=40.00%, “Netral”=5.45%, “Tidak Setuju”=0%, “Sangat Tidak Setuju”=0%
Data Distribusi pada Jumlah Kasus Penyakit Tuberculosis (TBC) Wilayah Kabupaten/Kota di Jawa Barat pada periode 2016 hingga 2021 Jasmin, Nur Shabrina; Rianto, Rianto; Purwayoga, Vega
Jurnal Ilmiah Teknik Informatika & Elektro (JITEK) Vol 3, No 2 (2024): Jurnal Ilmiah Teknik Informatika & Elektro (JITEK)
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jitek.v3i2.1845

Abstract

Tuberculosis (TB) or Mycobacterium Tuberculosis is a germ that causes Tuberculosis infection that is contagious and attacks anyone. It is still a public health problem in Indonesia. The number of TB cases in West Java is high with a total of 101,272 TB cases that continue to increase by 21.12% each year. Therefore, it is necessary to analyze the normality test on the data of the number of TB cases in West Java to determine whether the data comes from a population that has a normal distribution or not. The method used in this research uses a qualitative method by observing 'OPEN DATA JABAR". The results of the normality test research by knowing the distribution of data using wrangling data and profiling data are known that the data is not normally distributed.
Comparison of Support Vector Machine, Random Forest and XGBoost for Sentiment Analysis on Indodax Naufalino, Moch. Alfarros Difa; Al-husaini, Muhammad; Rianto, Rianto
Journal of Computer Networks, Architecture and High Performance Computing Vol. 7 No. 2 (2025): Research Article, Volume 7 Issue 2 April, 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v7i2.5894

Abstract

The rapid growth of digital assets like Bitcoin and cryptocurrencies has increased the need for secure trading platforms such as Indodax. With the growing number of users, reviews on platforms like Google Play Store provide valuable insights into user experience and satisfaction. This research applies Machine Learning methods to classify user review sentiments by comparing three main algorithms Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost). One of the main challenge in sentiment analysis is the presence of irrelevant or redundant features, which can reduce model accuracy and increase computational costs. The Feature Selection Chi-Square technique is used to filter the most influential features, enhancing model efficiency without losing critical information. Experimental results show that SVM delivers the best performance compared to Random Forest and XGBoost. Before applying Chi-Square, SVM achieved 91% accuracy, which increased to 94% after applying the feature selection technique. The number of features used was reduced from 52,312 to 2,000 without significant information loss. This combination of SVM and Feature Selection Chi-Square proves to be an efficient and accurate solution for analyzing user sentiment on crypto trading platforms like Indodax. This method is expected to improve the responsiveness of trading applications to user needs and serve as a foundation for further research in Machine Learning-based sentiment analysis.
SOCA-YOLO: Smart Optic with Coordinate Attention Model for Vision System-Based Eye Disease Detection Rianto, Rianto; Purwayoga, Vega; Aradea; Mikail, Ali Astra; Yumna, Irsalina
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.29293

Abstract

Purpose: The purpose of this research is to identify eye diseases using a modified YOLOv9. In particular, we modified YOLOv9 with the addition of Coordinate Attention (CA) for better eye disease detection performance, the use of Programmable Gradient Information (PGI), and Generalized Efficient Layer Aggregation Network (GELAN) for higher computational efficiency and accuracy. Methods: This study consists of several stages, including the acquisition of eye disease data obtained from the Roboflow website, data annotation, image augmentation, modeling using a modified YOLOv9, and model evaluation. Result: SOCA-YOLO model achieved an F1 score of 87,2% and mAP50 of 92,9%, outperforming YOLOv9-e by 1,7%. It also surpassed YOLOv6-L6 by 11,1%, YOLOv10-X by 0,8% in mAP50, and YOLOv8-X by 1,1% in recall, showcasing its superior detection accuracy and recall performance. Novelty: This research contributes by introducing the SOCA-YOLO model in improving the performance of the YOLOv9 by modifying the addition of Coordinate Attention (CA) for better eye disease detection performance, alongside Programmable Gradient Information (PGI) and Generalized Efficient Layer Aggregation Network (GELAN) for better computational efficiency and accuracy.
Perbandingan Algoritma Machine Learning Untuk Prediksi Gagal Bayar Pinjaman Koperasi yang Optimal Aziz, Hilmi; Rianto, Rianto
FORMAT Vol 13, No 2 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2024.v13.i2.001

Abstract

Abstract - Predicting loan repayment defaults is quite an important thing to do in a financial institution such as a Savings and Loans Cooperative. The aim is to minimize the occurrence of loan defaults by borrowers to cooperatives so that bankruptcy does not occur. In this study, the development of a predictive model was carried out using several popular machine learning algorithms, namely logistic regression, decision tree, random forest and k-nearest neighbors (KNN), then the four models were compared and evaluated in order to find out which model with the most effective algorithm. in predicting loan defaults in cooperatives. Program evaluation is carried out by metrics such as accuracy, precision, recall, and f1-score. The dataset itself is obtained from a loan list which includes attributes such as borrower profile, loan amount, number of installments, etc. This dataset is divided into training data and test data to train and evaluate the model. The results showed that the Random Forest algorithm model provided the best accuracy, reaching 89%, followed by the Decision Tree with the highest accuracy value, which reached 84%, and finally Logistic Regression and K-Nearest Neighbors with the same accuracy value, namely 81%. These four algorithms were chosen because they are well-known algorithms among other algorithms for financial predictions because of their ability to understand complex relationships, provide interpretable results, overcome overfitting problems, and consider the interrelationships between similar entities. Abstrak – Melakukan prediksi kegagalan pembayaran pinjaman merupakan hal yang cukup penting untuk dilakukan di sebuah badan keuangan seperti Koperasi Simpan Pinjam. Tujuannya yaitu untuk meminimalisir terjadinya gagal bayar pinjaman oleh peminjam kepada Koperasi agar tidak terjadi bangkrut. Pada penelitian ini dilakukan pengembangan model prediksi dengan menggunakan beberapa algoritma machine learning yang cukup popular yaitu  logistic regression, decision tree, random forest dan k-nearest neighbors (KNN), kemudian keempat model tersebut dibandingkan dan dievaluasi agar diketahui model dengan algoritma mana yang paling efektif dalam memprediksi gagal bayar pinjaman di Koperasi. Evaluasi program dilakukan metrik-metrik seperti akurasi, presisi, recall, dan f1-score. Untuk datasetnya sendiri didapat dari daftar pinjaman yang mencakup atribut seperti profil peminjam, jumlah pinjaman, banyak angsuran, dll. Dataset ini dibagi menjadi data pelatihan dan data uji untuk melatih dan mengevaluasi model. Hasil penelitian menunjukkan bahwa model algoritma Random Forest memberikan akurasi terbaik yaitu mencapai 89%, diikuti oleh Decision Tree dengan nilai akurasi tertingginya yang mencapai 84%, dan yang terakhir Logistic Regression dan K-Nearest Neighbors dengan nilai akurasi yang sama yaitu 81%. Keempat algoritma ini dipilih karena merupakan algoritma yang cukup terkenal di antara algoritma lainnya untuk prediksi dalam hal keuangan karena kemampuan mereka untuk memahami hubungan yang kompleks, memberikan hasil yang dapat diinterpretasikan, mengatasi masalah overfitting, dan mempertimbangkan keterkaitan antara entitas yang serupa.
Sentiment Analysis Of Student Opinion Related To Online Learning Using Naïve Bayes Classifier Algorithm And SVM With Adaboost On Twitter Social Media Ramli, Mohammad Rizal; Sulastri, Heni; Rianto, Rianto
Telematika Vol 20 No 2 (2023): Edisi Juni 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i2.8827

Abstract

Twitter is one of the social media that functions to express opinions on issues or problems that are currently happening, such as problems in the social, economic, educational and other fields. One of the issues being discussed so far is online learning. The government has issued a policy, one of which is for all students to study at home online by using a network to be able to interact with each other like in the classroom. The government's reason for issuing this policy is to break the chain of the spread of the Covid-19 virus, which until now has not subsided. Regarding this online learning policy, there are pros and cons. This opinion is widely expressed on social media, one of which is Twitter. Sentiment analysis is a method for analyzing an opinion which aims to classify texts. The Naïve Bayes Classifier and Support Vector Machine methods are methods machine learning that can be used for sentiment analysis. The problem in classifying text is that the resulting accuracy is less than optimal, so feature selection or boosting is needed to improve its accuracy. In this study, optimization of boosting was carried out using Adaboost. The purpose of this study is to compare the performance of the algorithm before and after using Adaboost. The results of the sentiment analysis on online learning obtained the highest accuracy results by the Naïve Bayes Classifier algorithm coupled with Adaboost of 99.26%, with a precision of 99.39% and recall of 99.20%.
Co-Authors Acep Irham Gufroni Al-Husaini, Muhammad Alam Rahmatulloh Alfarez, Rifky Algi Muhkamaat Abdillah Aradea Aradea Ari Sandriana Aziz, Hilmi Baetulloh, Uci Desi Purnamasari Dwi Ramti Asih Fauzi Athallah, Zico Firmansah, Teguh Anugrah Firmansyah Maulana Fransiska, Susanti Galih Permana Givany Salsabila Siti Rukoyah Gunawan, Rohmat Gunawan, Rohmat Hadi Purwadi Heni Sulastri Husni Mubarok Husni Mubarok Husni Mubarok Husni Mubarok Hutari Maulida Kurnia Iqbal Muhammad Fajar Nuralam Jasmin, Nur Shabrina Kurnia, Hutari Maulida Lusi Robiatul Fadilah Meidiyan, Mohamad Gibran Mikail, Ali Astra Mohammad Rizal Ramli Muhammad Adi Khairul A Muhammad Adi Khairul Anshary Naufalino, Moch. Alfarros Difa Neng Ika Kurniati Nugraha, Cindera Syaiful Nur Widiyasono Oktalia, Silvia Dwi Purwayoga, Vega R. Moch. Dysa Satria Anggara Rahmi Nur Shofa Ramli, Mohammad Rizal Rizal, Randi Ruswati, Ruswati Shihab, Muhammad Quraish Shofa, Rahmi Nur Shofa, Rahmi Nur Silvia Dwi Oktalia Siti Rukoyah, Givany Salsabila Sulastri, Heni Yumna, Irsalina Yustikasari, Yusi