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All Journal Elkom: Jurnal Elektronika dan Komputer INFOKAM Jurnal Informatika dan Teknik Elektro Terapan RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Sains dan Informatika IKRA-ITH Informatika : Jurnal Komputer dan Informatika Journal of Information Systems and Informatics bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Ilmiah Intech : Information Technology Journal of UMUS Jurnal Digit : Digital of Information Technology Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Education Technology Information Social Sciences and Health Jurnal Indonesia Sosial Sains JAMU : Jurnal Abdi Masyarakat UMUS Innovative: Journal Of Social Science Research Jurnal Teknik Indonesia Jurnal Multidisiplin Teknologi dan Arsitektur Journal of Citizen Research and Development Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer Jurnal Publikasi Teknik Informatika
Otong Saeful Bachri
Program Studi Teknik Informatika, Fakultas Teknik, Universitas Muhadi Setiabudi, Brebes, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Other

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Penerapan Metode TOPSIS Pada Sistem Pendukung Keputusan Berbasis Web Untuk Penentuan Pegawai Terbaik Di Puskesmas Larangan Brebes Fa Wa Id Ali Ersadi, Fawaid; Otong Saeful Bachri; Nur Ariesanto Ramdhan
Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer Vol. 5 No. 2 (2025): Juli: Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/juritek.v5i2.5014

Abstract

Pemilihan pegawai terbaik di Puskesmas Larangan Brebes selama ini masih dilakukan secara subjektif oleh Kepala Tata Usaha tanpa kriteria yang jelas. Penelitian ini bertujuan untuk mengembangkan sistem pendukung keputusan berbasis web dengan metode Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) untuk membantu manajemen menilai kinerja pegawai secara objektif dan transparan. Sistem mempertimbangkan tujuh kriteria: presensi, kedisiplinan, kinerja, sikap, kerja tim, inisiatif, dan masa kerja. TOPSIS dipilih karena kemampuannya dalam menentukan alternatif terbaik berdasarkan jarak dengan solusi ideal positif dan negatif. Hasil implementasi menunjukkan sistem dapat memberikan peringkat pegawai dengan cepat, akurat, dan dapat diakses melalui web. Contoh hasil menunjukkan Pegawai A meraih nilai preferensi tertinggi (0,7857), diikuti Pegawai B (0,6800), dan Pegawai C (0,6577). Sistem juga dilengkapi fitur visualisasi dan ekspor laporan Excel. Dengan demikian, sistem ini diharapkan meningkatkan objektivitas penilaian dan motivasi pegawai
Klasifikasi Penyakit pada Daun Padi Menggunakan Teknik Pengolahan Citra dan Convolutional Neural Network Firman Putra Perdana; Bambang Irawan; Otong Saeful Bachri
Jurnal Publikasi Teknik Informatika Vol. 5 No. 1 (2026): Januari: Jurnal Publikasi Teknik Informatika
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupti.v5i1.6694

Abstract

Rice cultivation plays a crucial role in national food security, but is often hampered by leaf disease attacks that significantly impact production decline. To address this challenge, this study designed an application based on the Convolutional Neural Network (CNN) algorithm to classify rice leaf diseases automatically and accurately. Data collection was conducted through direct observation at Gapoktan (Farmer Group Association) in Kuningan Regency, interviews with farmers, literature studies, and system development using the Rapid Application Development (RAD) approach, which enables rapid and structured application development. The CNN model was tested using 480 sample images and achieved a high accuracy of 97.75%. The F1-Score values ​​obtained were 0.97 for Brown Spot, 0.921 for Blast, 0.871 for Hispa, and 0.952 for healthy leaves. These results indicate that the application is capable of early disease detection, enabling farmers to take immediate preventive measures to minimize crop losses. To improve performance, it is recommended to apply optimization techniques to the CNN model, such as dataset expansion, various dataset augmentation techniques, and evaluation of high-complexity images. Development into other disease classifications is also highly potential. Overall, this application has significant potential to support digital agriculture and realize a more sustainable and modern rice farming system.
Implementasi Sistem Informasi Pelaporan Masyarakat Real-Time Berbasis Website Pemerintah Kabupaten Brebes Menggunakan Metode Waterfall: Pemerintah Kabupaten Brebes Sarah Hasan; Agyztia Premana; Otong Saeful Bachri
Jurnal Teknik Informatika UMUS Vol 7 No 2 (2025): November
Publisher : Universitas Muhadi Setiabudi

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

Abstract

Surat meyurat menjadi kebutuhan penting oleh masyarakat dalam kepengurusan keperluannya, di Perumahan Muara Sarana Indah masih belum tersistemasinya atau masih dilakukan secara manual mengenai layanan surat menyurat yang melibatkan Ketua Rt dan Ketua Rw setempat dan arsip surat yang belum tesimpan dengan rapi. Tujuan penelitian adalah membuat sebuah sistem layanan masyarakat berbasis web menggunakan metode watefall. Metode ini digunakan untuk mengembangkan sistem perangkat lunak dengan mempunyai alur hidup perangkat lunak secara bertahap dimulai dari analisis, design, pengkodean dan pengujian. Dalam merancang sistem yang digunakan adalah bahasa pemrograman database MySQL dan PHP. Dan pengujian untuk sistem menggunakan Black-box testing dengan pendekatan Equivalence Partitioning sedangkan pengujian yang dilakukan oleh pengguna menggunakan User Acceptance Testing (UAT). Penelitian ini menghasilkan Sistem Layanan Warga berbasis web yang mempermudah warga dalam mengurus surat – menyurat dan mempermudah Ketua RW dan Ketua RT dalam menyimpan arsip surat yang sudah diakses oleh warganya.
Perbandingan Kinerja Multinomial Naïve Bayes pada Variasi Rasio Data Training dan Testing dalam Analisis Sentimen Ulasan Taman Rakyat Slawi Ayu Moh. Syaogi; Nur Ariesanto Ramdhan; Otong Saeful Bachri
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.6941

Abstract

Sentiment analysis is one of the text mining techniques used to identify public opinions toward a particular object or service. Taman Rakyat Slawi Ayu, as one of the public green spaces in Tegal Regency, receives numerous visitor reviews through Google Maps, which can be utilized as a source of information to evaluate the quality of its services and facilities. This study aims to compare the performance of the Multinomial Naïve Bayes algorithm using different training and testing data split ratios for sentiment analysis of Taman Rakyat Slawi Ayu reviews. The data were collected through a web crawling technique on Google Maps, resulting in 4,153 reviews. After the data selection process, 1,794 reviews met the analysis criteria. The preprocessing stage consisted of cleaning, case folding, tokenizing, stopword removal, and stemming, resulting in 1,789 reviews ready for classification. Sentiment labeling was performed based on user ratings, where ratings of 1–3 were categorized as negative sentiment and ratings of 4–5 as positive sentiment. The experiments were conducted using three data split ratios: 70:30, 80:20, and 90:10. The results showed that the 90:10 ratio achieved the highest accuracy of 87.71%, with a precision of 0.88, recall of 0.99, and F1-score of 0.93. However, this ratio used a smaller testing dataset, making the evaluation results less representative than those obtained using the other data split ratios. Overall, the study demonstrates that variations in the training and testing data split ratio affect the classification performance of the Multinomial Naïve Bayes algorithm in sentiment analysis of Taman Rakyat Slawi Ayu reviews.
Implementasi Metode MOORA pada Sistem Prediksi Varietas Padi Berdasarkan Karakteristik Tanah Eko Fuji Pangestu; Otong Saeful Bachri; Agyztia Premana
IKRA-ITH Informatika : Jurnal Komputer dan Informatika Vol. 10 No. 2 (2026): IKRAITH-INFORMATIKA Vol 10 No 2 Juli 2026
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikraith-informatika.v10i2.7271

Abstract

Rice crop productivity is influenced by the balance between the cultivated variety and soil characteristics. In Tanjung District, Brebes Regency, the selection of rice varieties is largely based on farmer experience, often without objectively considering soil conditions. This study aims to implement the Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method to determine suitable rice varieties based on soil characteristics. The study utilizes five criteria soil pH, effective soil depth, soil moisture, drainage conditions, and soil texture and evaluates ten alternative rice varieties. The MOORA method involves constructing a decision matrix, normalization, weighting, calculating optimization values ​​(Yi), and ranking the alternatives. The results indicate that the Ciherang, IR64, Inpari 32 HDB, Inpari 34 Salin Agritan, and Inpari 35 Salin Agritan varieties achieved the highest optimization value (Yi) of 0.3441, making them the recommended varieties best suited to the analyzed soil characteristics. The application of the MOORA method provides objective and systematic rice variety recommendations, thereby supporting the decision-making process for variety selection based on soil conditions.
SISTEM PENDUKUNG KEPUTUSAN REKOMENDASI JUDUL BUKU PERPUSTAKAAN MENGGUNAKAN METODE ANALYTIC HIERARCHY PROCESS Desi Permata Sari; Otong Saeful Bachri; Bambang Irawan
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 3 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i3.10668

Abstract

Dinas Perpustakaan dan Arsip Kabupaten Brebes menghadapi permasalahan fluktuasi dan penurunan jumlah pengunjung dalam enam tahun terakhir, yang salah satu faktornya adalah belum adanya mekanisme rekomendasi buku yang objektif dan sistematis. Penelitian ini bertujuan mengembangkan Sistem Pendukung Keputusan rekomendasi judul buku menggunakan metode Analytical Hierarchy Process (AHP) dengan mengintegrasikan empat kriteria, yaitu kesesuaian genre, tahun terbit, popularitas, dan rating. Metode penelitian meliputi identifikasi masalah, pengumpulan data melalui observasi dan wawancara, penentuan kriteria dan subkriteria, perhitungan bobot AHP, pengujian konsistensi, serta perangkingan alternatif. Hasil penelitian menunjukkan bahwa kesesuaian genre memiliki bobot tertinggi (0,5579), diikuti tahun terbit (0,2633), popularitas (0,1219), dan rating (0,0569). Seluruh perhitungan dinyatakan konsisten dengan nilai CR < 0,1. Buku Laut Bercerita memperoleh nilai preferensi tertinggi (0,6675) sebagai rekomendasi utama. Sistem berbasis web berhasil diimplementasikan dan menghasilkan output sesuai perhitungan manual. Penelitian ini berkontribusi dalam menyediakan sistem rekomendasi buku yang objektif dan terukur, sehingga membantu pustakawan memberikan rekomendasi secara cepat serta memudahkan pengguna menemukan koleksi sesuai kebutuhan.
Co-Authors Abdi R, Aulia Agung Permana, Tegar Agyztia Premana Ahmad Najmi Fuadi Almesta, Adhika Yameraja Andi Yulianto Anin, Anindia Anindia Ardiani Apriliani, Rosema Dina Ariestanto Ramdhan, Nur Badruzzaman, Ni'am Bagus W, Teguh Bambang Irawan Bambang Irawan Bambang Irawan /E21207042 Budiasih, Kristanti Catur Edi Widodo Desi Permata Sari Dumadi, Dumadi Eko Fuji Pangestu Elis Afriyani Fa Wa Id Ali Ersadi, Fawaid Fery Sofian Efendi Firman Putra Perdana Fitro, Achmad Gagas Prasetya Gian Fitralisma Harliana Harliana Harliana Harliana Irmansyah, Teguh Bagus Wicaksono Iza Azkiah Khalid Iskandar Khalid Iskandar Kharisma Dwi Pratiwi Moh. Syaogi Muhami, Rizal Muhammad Bagus Irwana Aji Muhammad Lutfi Jamaludina Mukson Mukson Nadia Nur Alifiana Nur Ariesanto Ramdhan Nur Ariesanto Ramdhan Nur Ariesanto Ramdhan Nurfikri, Muhammad Ihsan Oky Dwi Nurhayati Pandhu Wijaya, Akhmad Pratama Putra, Rizki Premana, Agyztia R.M.Herdian Bhakti Rahayu Ramdhan, Nur Ariesanto Rifqoh Febriyanti Putri Riono, Slamet Bambang Rizky Esa Riyanto Roby Setiadi Roby Setiadi Rohman, Aulia Abdi Rudianto Rudianto Sarah Hasan Shahib, M Umar Sri Yulianti Susilo, Bayu Tegar Agung Permana Teuku Rizal Adi Pangestu Tri S, Wananda Trisena Pramuja Wahyuningsih, Puji Wiwik Astriani Yenny Ernitawati Yuhano Yuhano