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All Journal Jupiter ELSE (Elementary School Education Journal) : Jurnal Pendidikan dan Pembelajaran Sekolah Dasar Technomedia Journal JSiI (Jurnal Sistem Informasi) Jurnal Bahtera - Jurnal Pendidikan Bahasa Sastra dan Budaya Journal of Information Systems and Informatics Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Tika Jurnal Pengabdian Masyarakat (ABDIRA) Jurnal Mandiri IT Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Algoritma J-Icon : Jurnal Komputer dan Informatika Indonesian Journal of Community Empowerment (IJCE) "JAMASTIKA" Jurnal Mahasiswa Teknik Informatika Jurnal Bakti Humaniora Ngudi Waluyo Empowerment : Jurnal Pengabdian Kepada Masyarakat INOVTEK Polbeng - Seri Informatika Proceeding International Collaborative Conference on Multidisciplinary Science Jurnal Ilmu Komputer ITEJ (Information Technology Engineering Journals)
Yoannes Romando Sipayung
Universitas Ngudi Waluyo
Aerospace Engineering Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Social Sciences Other

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Prediksi Tingkat Kepadatan Kendaraan Menggunakan Metode YOLO Berbasis Image Processing pada Video Jalan Raya Nitral Sejak Terang Waruwu; Yoannes Romando Sipayung
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3152

Abstract

Traffic congestion in urban areas causes time losses, increased fuel consumption, and a decline in environmental quality. Therefore, a reliable visual data-based traffic monitoring system is needed. This study develops a system for detecting and analyzing traffic density and identifying peak hours by utilizing the You Only Look Once (YOLO) algorithm as a deep learning approach. YOLO is used to detect and count vehicles from highway video data, and the detection results are stored in a database for temporal analysis using historical vehicle volume data. This analysis aims to identify traffic density patterns and rush hour periods without applying a time-series-based temporal prediction model. The system's performance is evaluated using precision, recall, and mean Average Precision (mAP) metrics, while the rush hour identification results are validated through comparison with field observations. Test results show that YOLO is capable of accurately detecting vehicles and that the developed system can consistently identify periods of traffic density. The integration of YOLO-based vehicle detection with web-based temporal analysis is expected to support travel decision-making in urban environments.
Sistem Pengajuan Izin Kerja Karyawan Berbasis Web dengan Prediksi Pola Perizinan Menggunakan Algoritma Support Vector Machine (SVM) Ningsihati Halawa; Yoannes Romando Sipayung
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3241

Abstract

The implementation of conventional work permit management systems often causes problems, such as delays in approval, the risk of document loss, and limitations in real-time monitoring of permit status. This study developed a web-based work permit management system that automates administrative processes and supports managerial decision-making through historical data analysis. The system was developed using the Waterfall method and implemented with a client–server architecture, while the Support Vector Machine (SVM) algorithm with Radial Basis Function (RBF) kernel was applied to classify permit patterns. Test results show that the SVM model is capable of achieving 99.33% accuracy in testing data classification. The findings of this study indicate that the integration of web-based systems with predictive algorithms can improve the efficiency of the licensing process while providing a scientific framework for analyzing labor licensing patterns, thereby contributing to the development of data-based human resource management methods.
Pelatihan Membuat Website Sekolah Sekolah dengan Menggunakan Blooger di Komunitastas e-guru.id Abdul Rohman; Yoannes Romando Sipayung; Basuki Sulistio
Multimatrix: Jurnal Ilmu Komputer Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Ngudi Waluyo

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Website sekolah merupakan instrumen vital dalam era transformasi digital untuk mendukung transparansi informasi, promosi, dan komunikasi antara sekolah dengan masyarakat. Namun, keterbatasan kompetensi teknis guru dan kendala biaya seringkali menjadi hambatan utama bagi sekolah dalam memiliki website resmi. Kegiatan Pengabdian kepada Masyarakat (PkM) ini bertujuan untuk meningkatkan literasi digital guru serta memberikan keterampilan praktis dalam pembuatan dan pengelolaan website sekolah berbasis platform Blogger. Kegiatan ini melibatkan 79 guru dari berbagai wilayah di Indonesia yang tergabung dalam komunitas e-Guru.id. Metode pelaksanaan dilakukan secara daring selama enam bulan pada tahun 2024 dengan pendekatan partisipatif dan learning by doing, yang mencakup tahap persiapan, pelatihan inti, pendampingan, serta evaluasi. Hasil kegiatan menunjukkan bahwa seluruh peserta berhasil membuat website sekolah secara mandiri yang dilengkapi dengan fitur profil sekolah, berita, dan layanan interaktif tanpa memerlukan keahlian pemrograman (coding). Selain itu, terdapat peningkatan signifikan pada kepercayaan diri dan kompetensi guru dalam pengelolaan konten digital. Penggunaan Blogger terbukti menjadi solusi praktis, efisien, dan berkelanjutan untuk mengatasi kesenjangan digital di lingkungan pendidikan. Kata Kunci: Blogger, Kompetensi Guru, Literasi Digital, Transformasi Digital, Website Sekolah. School websites are vital instruments in the era of digital transformation, supporting information transparency, promotion, and communication between schools and the community. However, limited teacher technical competency and financial constraints are often major barriers for schools in establishing official websites. This Community Service (PkM) activity aims to improve teachers' digital literacy and provide practical skills in creating and managing school websites based on the Blogger platform. This activity involved 79 teachers from various regions in Indonesia who are members of the e-Guru.id community. The implementation method was conducted online for six months in 2024, using a participatory and learning-by-doing approach, encompassing preparation, core training, mentoring, and evaluation. The results showed that all participants successfully created their own school websites, complete with school profile features, news, and interactive services without requiring programming skills (coding). Furthermore, there was a significant increase in teachers' confidence and competency in digital content management. The use of Blogger has proven to be a practical, efficient, and sustainable solution to address the digital divide in educational settings. Keywords: Blogger, Teacher Competence, Digital Literacy, Digital Transformation, School Website.
Utilization of Tapertis Educational Technology in Learning Multiplication Concepts in Elementary Schools Zulmi Roestika Rini; Yoannes Romando Sipayung; Abdul Rahman; Kartika Yuni Purwanti; Himmah Taulany; Anni Malihatul Hawa; Ella Suryani; Nurfitriyani Kartika Dewi; Hesti Yunitiara Rizqi; Swantika Ilham Prahesti; Nico Irawan
Proceeding International Collaborative Conference on Multidisciplinary Science Vol. 2 No. 2 (2025): December : ICCMS (Proceeding International Collaborative Conference on Multidis
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/iccms.v2i2.214

Abstract

This study aims to determine the effectiveness of using the Jarimatika method combined with Tapertis media in improving elementary school students' understanding of multiplication concepts. This study uses a quantitative approach with a pre-experimental intact group comparison type design. The research subjects consisted of 17 fourth-grade students of SDN Candigaron 03 who were divided into two groups, namely the experimental group and the control group. Data collection instruments were in the form of tests (pre-test and post-test) and observation sheets. Experts carried out media validation with an average result of 56.33 (outstanding category). Data analysis used paired sample t-test, t-test, and simple linear regression. The results showed a significant difference between the pre-test and post-test results in both groups. The average post-test score of the experimental group was higher (84.89) than the control group (77.12). The regression test showed that Tapertis media significantly affected understanding of multiplication concepts (p < 0.05). This study concludes that the Jarimatika method, supported by Tapertis media, is effective for use in mathematics learning to improve elementary school students' understanding of multiplication concepts.
Evaluation of Employee Payroll Decision-Making System at PT Morich Indo Fashion using Machine Learning Efaforito Gulo; Yoannes Romando Sipayung
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/5p7nse86

Abstract

PT Morich Indo Fashion is a company that focuses on clothing production and includes fabric inspection, accessories, and heat transfer storage. This study aims to evaluate the use of payroll information systems in internal control. The method used in this research is descriptive with a qualitative approach, and the data obtained comes from secondary and primary sources. The problem faced by the Cooperative is that there are errors in calculating employee salaries and lack of clarity in the payroll process, where employees are only told the total amount of salary each month without knowing the amount of deductions caused by lateness or absenteeism in a month. To overcome this problem, a web-based payroll information system is needed. This research aims to design and develop a web-based employee payroll system at PT Morich Indo Fashion, with the aim of speeding up and simplifying the salary payment process effectively. This research uses a qualitative method with the Design and Creation approach and applies the waterfall development method. Testing is done with White-box Testing and Black-box Testing techniques. The results of the test show that the objectives of this research have been achieved. This application successfully simplifies and accelerates the process of calculating employee salaries in a transparent, accurate, effective, and efficient way.
DEVELOPMENT OF AN EXPERT SYSTEM FOR DIAGNOSING EGGPLANT DISEASES USING THE TSUKAMOTO FUZZY LOGIC METHOD Efrizal Yudhantoro; Yoannes Romando Sipayung
Jurnal Sistem Informasi Vol. 13 No. 1 (2026)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/6ddknb34

Abstract

Eggplant diseases are a major factor contributing to decreased crop quality and yield, particularly among novice farmers with limited knowledge of early disease identification. The uncertainty of symptom manifestation and limited access to agricultural experts further increase the risk of crop failure. This study aims to develop a web-based expert system for diagnosing eggplant diseases using the Tsukamoto Fuzzy Logic method. The novelty of this research lies in the integration of weighted symptom severity, fuzzy inference rules, and confidence-level outputs into a practical decision-support system specifically designed for eggplant disease diagnosis. The research adopts the Waterfall development model, including requirements analysis, system design, implementation, and testing. The knowledge base consists of five main diseases and twenty symptoms with weighted values ranging from 0.55 to 1.00. System evaluation using Black Box Testing shows that 100% of functional features operate successfully according to system requirements. Furthermore, diagnostic results demonstrate high confidence levels, reaching up to 97% for certain disease cases, indicating reliable system performance in handling uncertainty. This study contributes to the development of intelligent agricultural decision-support systems by providing an accessible, accurate, and efficient diagnostic tool. The proposed system can assist farmers in early disease detection, reduce dependency on experts, and potentially minimize crop losses while improving eggplant productivity.   Keywords: Expert System, Eggplant Disease, Tsukamoto Fuzzy Logic, Decision Support System, Smart Agriculture
Sentiment Analysis of Instagram Users Toward the Animated Film Merah Putih: One For All Using the Naïve Bayes Method Nabila Wafa; Yoannes Romando Sipayung
ITEJ (Information Technology Engineering Journals) Vol. 11 No. 1 (2026): June
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v11i1.295

Abstract

Instagram is a widely used social media platform where users express opinions on various forms of entertainment, including animated films. The animated film Merah Putih One For All, as one of Indonesia’s local animation works, has received diverse responses from Instagram users that reflect positive, neutral, and negative sentiments. This study aims to analyze and classify the sentiment of Instagram user comments related to the film using the Naïve Bayes algorithm. This research utilized 200 Instagram comments, which were categorized into three sentiment classes. Text preprocessing was applied prior to classification. The dataset was evaluated using the Split Validation method with a 60:40 ratio, where 60% of the data were used for training and 40% for testing. Model performance was assessed using a confusion matrix along with accuracy, precision, and recall metrics. The experimental results show that the Naïve Bayes algorithm achieved an accuracy of 76,67%. The positive sentiment class obtained the highest recall value of 100%, followed by the neutral class with 83,33%, while the negative sentiment class recorded the lowest recall at 46,67%. These findings indicate that the model performs better in identifying positive and neutral sentiments than negative sentiment. Overall, the results demonstrate that the Naïve Bayes algorithm is sufficiently effective for sentiment analysis of Instagram comments, although further improvements are required to enhance the classification of negative sentiment.  
Co-Authors Abdul Rahman Abdul Rohman Agung Wibowo Anni Malihatul Hawa Basuki Sulistio Bayu Evendi Budiati Budiati Desi Fitria Wulandika Dimas Fajar Fiandaru Efaforito Gulo Efaforito Gulo Efrizal Yudhantoro Ella Suryani Fauziah , Syifa Galih Agil Febri Hesti Yunitiara Rizqi Himmah Taulany Ign.F. Bayu Andoro.S Isnia Anjar Setyani Kartika Yuni Purwanti Keysa Kalina Elbaraka Krisna Cahya Ramadhan Kurnia Catur Saputra Lestari, Azizah Sella Lestari, Puji Lisa Virdinarti Putra Liyanovitasari, Liyanovitasari Lutfi Munawaroh Mauliza, Risha Nur Miski Aderisa Munawaroh, Lutfi Nabila Wafa Nico Irawan Ningsihati Halawa Nitral Sejak Terang Waruwu Nurfitriyani Kartika Dewi Prahesti , Swantyka Ilham Purnama, Arga Aditia Ramadhan, Krisna Cahya Reny Sulistyowati Rochmawati, Nur Intan Romandhoni, Fajar Taufik Sabilatul Hidayah Shofiyatul Latifah Sri - Mujiyono Suamanda Ika Novichasari Swantika Ilham Prahesti Tri Arie Bowo Tri Maula Nafi Ummi Nurul Hidayah Wafiq Lana Pradana Zulmi Restika Rini Zulmi Roestika Rini Zumrotul Chairiyyah