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All Journal Semantik Jurnal Simetris Jurnal Sains dan Teknologi Prosiding SNATIF Jurnal Edukasi dan Penelitian Informatika (JEPIN) Seminar Nasional Informatika (SEMNASIF) Proceeding SENDI_U Sistemasi: Jurnal Sistem Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JURNAL ILMIAH INFORMATIKA JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal DISPROTEK JSiI (Jurnal Sistem Informasi) Jurnal Nasional Komputasi dan Teknologi Informasi JSI (Jurnal sistem Informasi) Universitas Suryadarma Jurnal SITECH : Sistem Informasi dan Teknologi Jurnal Teknologi Informasi dan Pendidikan bit-Tech Abdimas Toddopuli: Jurnal Pengabdian Pada Masyarakat JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Pengabdian kepada Masyarakat Nusantara Journal of Information Technology Ampera JoMMiT : Jurnal Multi Media dan IT ABDIPRAJA (Jurnal Pengabdian kepada Masyarakat) Jurnal Abdi Masyarakat Indonesia International Journal of Management Science and Information Technology (IJMSIT) Muria Jurnal Layanan Masyarakat Jurnal Minfo Polgan (JMP) Jurnal Locus Penelitian dan Pengabdian Jurnal Teknik Informatika Bhakti Nagori (Jurnal Pengabdian kepada Masyarakat) Jurnal Algoritma Journal of Scientech Research and Development Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Teknik Indonesia Jurnal Sistem Informasi dan Manajemen Prosiding Seminar Nasional Unimus Media Elektrik Jurnal Publikasi Sistem Informasi dan Manajemen Bisnis Journal of Vocational, Informatics and Computer Education ABDINE :Jurnal Pengabdian Masyarakat
R Rhoedy Setiawan
Universitas Muria Kudus
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Pengelompokan Permintaan Produk Alat Kesehatan Menggunakan K-Means untuk Jadwal Pembelian Vika Aulia Munawaroh; R Rhoedy Setiawan; Yudie Irawan
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.23-1.3360

Abstract

Fluctuations in the demand for medical devices can trigger the risk of stock shortages (stockouts) and overstock conditions, which may affect operational costs and the quality of distribution services. This study aims to classify medical device products at CV Patriot Kencana Medika Kudus based on demand patterns and purchasing characteristics, and to map the clustering results as an initial basis for developing purchasing schedules. The data used consist of internal purchasing transaction histories from the 2023–2025 period with four main features: Quantity, Price_Per_Unit, Lead_Time_Days, and Total_Purchase_Value. The methods applied include exploratory data analysis, feature construction and normalization, determination of the optimal number of clusters using the Elbow Method and Silhouette Score, K-Means modeling, and evaluation using the Silhouette Score and Davies–Bouldin Index (DBI). The results indicate that the use of three clusters provides the most reasonable compromise between the inertia reduction pattern, Silhouette value, and managerial interpretability. A Silhouette Score of 0.2563 and a DBI value of 1.349 suggest that the quality of cluster separation remains at a low to moderate level, meaning that the resulting clusters are more appropriately interpreted as an initial segmentation rather than a fully distinct classification. The three clusters formed were interpreted as general products, premium products, and strategic products. The numerical characteristics of each cluster were then used to calculate simple indicators, namely the reorder point (ROP) and economic order quantity (EOQ), as baseline purchasing recommendations. The main contribution of this study lies in integrating clustering results with operational inventory policy parameters, although the findings still need to be interpreted cautiously because they have not yet been compared with other algorithms, their stability has not been tested, and the EOQ model applied remains simplified.
Klasterisasi Kebutuhan Pupuk Bersubsidi Menggunakan Algoritma K-Means dan Elbow Method Nurya Herlina Sari; R.Rhoedy Setiawan; Yudie Irawan
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.23-1.3361

Abstract

The distribution of subsidized fertilizer at the UD Barokah Tani Kiosk in Pati Regency does not yet meet farmers’ needs due to the manual management of RDKK data. This study aims to cluster subsidized fertilizer needs using the K-Means algorithm, validated by the Elbow Method and Silhouette Score. The data used consists of 1,420 RDKK records for the 2025–2026 period, with variables including land area, UREA_TOTAL, NPK_TOTAL, and the number of commodities. The results indicate that the optimal number of clusters is k = 3, with a Silhouette Score of 0.9192, indicating very high cluster quality. The data is divided into three categories: low, medium, and high, with a dominance in the low to medium categories. This study contributes by comprehensively integrating fertilizer requirement variables and using a combination of the Elbow Method and Silhouette Score to enhance the validity of the clustering results. The clustering results are implemented in a web-based system to support rapid, data-driven analysis and visualization.
Sistem Informasi Pengelolaan dan Pelaporan Aset BPR Mitra Kusuma Mandiri Diana Nur Yasmin; Rhoedy Setiawan
Abdimas Toddopuli: Jurnal Pengabdian Pada Masyarakat Vol. 7 No. 1 (2025): Volume 7, No 1, Desember 2025
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/atjpm.v7i1.7436

Abstract

Pengelolaan aset inventaris di Bank Perkreditan Rakyat (BPR) Kudus kini masih dilakukan secara manual dengan menggunakan catatan fisik dan spreadsheet. Hal ini menyebabkan risiko kesalahan manusia, duplikasi data, serta ketidakefisienan dalam proses pelaporan. Sebagai bentuk upaya peningkatan kualitas, akurasi, dan transparansi pengelolaan aset menjadi sangat penting untuk mendukung tata kelola perbankan yang baik dan mempersiapkan diri menghadapi audit eksternal. Penelitian ini bertujuan untuk merancang dan mengembangkan sistem informasi pengelolaan serta pelaporan aset inventaris berbasis web yang mampu meningkatkan efisiensi, akuntabilitas, dan kecepatan akses data aset di BPR Kudus. Metode penelitian yang digunakan mengikuti pendekatan pengembangan perangkat lunak melalui berbagai tahapan, yaitu analisis, desain, implementasi, dan pengujian. Pada tahap analisis, dilakukan observasi langsung serta wawancara terstruktur kepada lima responden yang dipilih secara purposif, terdiri dari staf administrasi umum, kepala bagian umum, dan pimpinan BPR, untuk mengidentifikasi kebutuhan fungsional sistem. Dalam tahap desain, digunakan diagram use case diagram, dan activity diagram. Implementasi sistem dilakukan dengan menggunakan framework Laravel (PHP) dan database MySQL. Hasil pengembangan menunjukkan bahwa sistem mampu mendukung fungsi pencatatan aset, pembaruan kondisi dan lokasi aset, pengajuan penghapusan berdasarkan alur persetujuan digital, serta pembuatan laporan otomatis dalam format PDF. Sistem ini memiliki potensi menjadi solusi digital yang realistis dan berkelanjutan bagi BPR skala kecil di Indonesia
Pengembangan dan Penerapan Sistem Logbook & Absensi Online sebagai Dukungan Monitoring Kegiatan Peserta Magang di BAZNAS Kudus Titis Chusnul Mahrom; R. Rhoedy Setiawan
Abdimas Toddopuli: Jurnal Pengabdian Pada Masyarakat Vol. 7 No. 1 (2025): Volume 7, No 1, Desember 2025
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/atjpm.v7i1.7545

Abstract

Kegiatan pengabdian ini dilaksanakan untuk merespons kebutuhan digitalisasi administrasi magang di BAZNAS Kabupaten Kudus yang selama ini masih mengandalkan sistem manual melalui presensi kertas dan logbook tertulis. Kondisi tersebut menimbulkan kendala seperti keterlambatan pelaporan, ketidakteraturan data, serta kesulitan pembimbing dalam memantau aktivitas peserta secara real-time. Kegiatan ini bertujuan untuk mengembangkan dan menerapkan sistem logbook dan absensi online yang dapat mendukung proses pencatatan kegiatan harian serta monitoring kehadiran peserta magang. Metode yang digunakan meliputi observasi alur kerja dan wawancara dengan admin, pembimbing, serta peserta magang untuk memetakan kebutuhan sistem, kemudian dilanjutkan dengan perancangan, pengembangan, dan pelatihan penggunaan sistem. Hasil kegiatan menunjukkan bahwa sistem berhasil diperkenalkan dan mulai dipahami oleh pengguna melalui sesi sosialisasi dan pendampingan, meskipun masih berada dalam tahap penerapan awal. Pengguna mulai mampu melakukan presensi digital, mengisi logbook harian, dan melihat rekap aktivitas secara otomatis. Sistem ini berpotensi meningkatkan efisiensi pengelolaan administrasi magang dan kualitas monitoring, meskipun evaluasi lanjutan masih diperlukan sebelum diimplementasikan secara penuh. Kesimpulan kegiatan menegaskan bahwa digitalisasi administrasi magang melalui sistem yang dikembangkan merupakan langkah awal penting menuju pengelolaan magang yang lebih modern, terstruktur, dan terintegrasi.
Analisis Pola Ko-Kemunculan Produk Berbasis Waktu Menggunakan Algoritma Apriori pada Data Penjualan Pitch 19 Rizky Muhammad Rizky Maulana; Muhammad Arifin; R. Rhoedy Setiawan
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 4 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i4.9817

Abstract

Penelitian ini menganalisis pola ko-kemunculan produk Pitch 19 periode Januari-April 2026 menggunakan market basket analysis dan algoritma Apriori. Data bersumber dari rekap penjualan harian pada sheet January-April, sehingga satu tanggal diperlakukan sebagai satu basket harian, bukan nota transaksi pelanggan. Tahapan penelitian meliputi preprocessing, transformasi data ke format long, pembentukan basket harian, segmentasi bulan dan jenis hari, penerapan Apriori, evaluasi support, confidence, dan lift, serta penyusunan rekomendasi. Hasil preprocessing menghasilkan 4.872 baris data dan 120 basket harian. Produk dominan meliputi Ayam Blackpepper, Ayam Asam Manis, Mineral Water, Cafe Latte, Americano, Golden Palm, Lychee Tea, Wing Feast, Red Velvet, dan Original Tea. Penjualan tertinggi terjadi pada Maret sebesar 9.692 item, sedangkan weekend mencapai 15.780 item dan lebih tinggi dibanding weekday sebesar 14.958 item. Penerapan Apriori pada 25 produk teratas dengan minimum support 0,30, minimum confidence 0,60, dan panjang itemset maksimum dua menghasilkan 276 frequent itemset. Banyak aturan menunjukkan ko-kemunculan produk dengan Mineral Water, tetapi nilai lift 1,000 menandakan hubungan tersebut bersifat umum karena Mineral Water muncul pada seluruh basket. Karena itu, hasil penelitian lebih tepat digunakan untuk pengelolaan stok, promosi weekend, paket produk terlaris, dan perbaikan pencatatan transaksi harian.
Implementasi Sistem Informasi Keuangan Berbasis Laravel Guna Mewujudkan Transparansi Pembayaran SPP dan Buku di MTs Al Fattah JuwanaImplementasi Sistem Informasi Keuangan Berbasis Laravel Guna Mewujudkan Transparansi Pembayaran SPP dan Buku di MTs Al Fattah Juwana Muhamad Yuwanda; Raden Rhoedy Setiawan
Jurnal Abdi Masyarakat Indonesia Vol 6 No 4 (2026): JAMSI - Juli 2026
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jamsi.3014

Abstract

Pengelolaan dana pendidikan, termasuk iuran SPP dan pembelian buku di MTs Al Fattah Juwana, selama ini masih mengandalkan metode konvensional berbasis buku besar dan kuitansi fisik. Praktik ini sering kali menimbulkan berbagai kendala operasional, mulai dari tingginya potensi kelalaian pencatatan (human error), lambatnya proses pelaporan finansial bulanan, hingga tidak adanya keterbukaan rincian tagihan untuk orang tua murid. Merespons persoalan tersebut, program pengabdian kepada masyarakat ini diinisiasi untuk merancang sekaligus menerapkan platform Sistem Informasi Keuangan Sekolah berbasis website menggunakan arsitektur Laravel. Kehadiran inovasi teknologi ini difokuskan untuk melakukan digitalisasi serta mendongkrak efektivitas manajerial keuangan di institusi tersebut. Skema pelaksanaan program disusun melalui empat fase krusial: analisis kebutuhan institusi mitra, instalasi perangkat lunak, pelatihan teknis secara hands-on, dan tahap supervisi berkelanjutan. Tingkat kesuksesan program ditinjau dari pencapaian target performa mitra serta tingkat kepuasan user. Berdasarkan hasil uji kompetensi pasca-pelatihan dan kuesioner kepuasan pengguna, seluruh target keberhasilan program terpenuhi secara maksimal (100%). Penerapan sistem ini secara radikal mengubah proses kerja staf tata usaha (TU) dan bendahara; dari yang semula menghabiskan waktu berhari-hari untuk merekap kuitansi fisik, kini mereka mampu mengeksekusi laporan bulanan secara otomatis dalam sekali klik serta melayani transaksi pembayaran siswa dengan lebih cepat. Integrasi sistem digital ini secara nyata membawa perubahan positif terhadap kecepatan layanan transaksi, akurasi kalkulasi dana, dan terwujudnya sistem administrasi madrasah yang modern serta transparan bagi seluruh pihak.
ANALISIS PERBANDINGAN KINERJA ALGORITMA SVM, NAÏVE BAYES, DAN KNN DALAM KLASIFIKASI SENTIMEN ULASAN APLIKASI PINTEREST DENGAN SMOTE DAN PSO Muhamad Dimas Firmansyah; R. Rhoedy Setiawan; Yudie Irawan
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7095

Abstract

The rapid growth of social media usage has led to a continuous increase in the volume of user reviews, necessitating automated analysis based on machine learning techniques. This study focuses on the development of a sentiment classification model for Pinterest application reviews on the Google Play Store by evaluating three algorithms: Support Vector Machine (SVM), Naïve Bayes, and K-Nearest Neighbors (KNN), combined with Synthetic Minority Oversampling Technique (SMOTE) and Particle Swarm Optimization (PSO). A total of 10,000 reviews were collected through web scraping and processed through preprocessing stages, lexicon-based labeling using InSet, TF-IDF feature extraction, and an 80:20 data split. SMOTE was first applied to balance the class distribution, followed by PSO for parameter optimization of each classification algorithm. The experimental results indicate that SVM achieved the best performance, attaining 95% accuracy with a more balanced F1-score after the application of SMOTE and PSO, while Naïve Bayes and KNN remained sensitive to class imbalance. As the final output, this study developed a Streamlit-based prediction dashboard to display sentiment results in real time, thereby supporting practical and efficient analysis of user perceptions. These findings confirm the effectiveness of combining SVM, SMOTE, and PSO as an optimal approach for sentiment classification on imbalanced review data.  
PENGELOMPOKAN BAHAN BAKU BERDASARKAN TINGKAT PENGGUNAAN BERBASIS ALGORITMA CLUSTERING PADA SELARAS COFFEE & SPACE Bayu Samudro Fadhilah; Muhammad Arifin; Rhoedy Setiawan
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8008

Abstract

Inventory management in the food and beverage business requires a measurable approach to reduce the risk of stock shortages and excess inventory. Selaras Coffee & Space has operational kitchen raw material data that can be utilized to identify usage patterns more objectively. This study aims to group raw materials based on usage levels by comparing K-Means, Hierarchical Clustering, and K-Medoids algorithms. The data were obtained from kitchen raw material stock opname and purchase order records for February 2026, using stock_fisik, min_stock, and qty_po as clustering attributes. The research stages included data collection, preprocessing, unique item aggregation, Min-Max normalization, clustering algorithm implementation, evaluation using Sum of Squared Errors (SSE) and Silhouette Score, and implementation of the results into a web-based system. The initial dataset consisted of 3,080 rows and was aggregated into 110 unique items. The evaluation results showed that K-Means and Hierarchical Clustering achieved an SSE value of 4.630818 and a Silhouette Score of 0.781801, indicating a strong cluster structure. K-Medoids obtained an SSE value of 11.022485 and a Silhouette Score of 0.470763. K-Means was selected as the best algorithm because it achieved optimal evaluation performance and is simpler to implement in the system. The clustering results showed that 6 items were categorized as High Usage, 7 items as Medium Usage, and 97 items as Low Usage. The results can assist management in understanding raw material usage levels as a basis for more effective inventory control.  
IMPLEMENTASI HYBRID AHP-TOPSIS PADA SISTEM PENDUKUNG KEPUTUSAN EVALUASI PERFORMA PRAMUDI Pratiwi Cahyaningtiyas; Rhoedy Setiawan; Soni Adiyono
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8210

Abstract

Subjective bias, delayed data accumulation, and unfair bonus allocation are common issues resulting from the manual pramudi appraisal method at PT Samudra Jaya Transport. To resolve these challenges, this research develops a web-based Decision Support System (DSS) integrating the Analytical Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The AHP method is employed to establish priority weights for five core criteria: cargo, attendance, discipline, fuel consumption, and fleet maintenance. Concurrently, TOPSIS is implemented to rank 107 pramudi partitioned into three distinct categories: New Pramudi, Senior Pramudi, and Experienced Pramudi. The AHP evaluation yields a reliable Consistency Ratio (CR) of 0.0259. Furthermore, the TOPSIS analysis identifies the leading preference scores for each cluster, specifically PB-01 at 0.7909, PS-01 at 0.8691, and PSE-01 at 0.9308. Black-Box testing confirms that all core system features function correctly. Ultimately, this system ensures a data-centric evaluation process, eliminates bias, and delivers highly transparent monthly bonus recommendations.
Integration of Artificial Intelligence and Blockchain in Inventory Systems for Enhanced Forecasting and Data Security R. Rhoedy Setiawan; Zainur Romadhon
Journal of Vocational, Informatics and Computer Education Vol 4, No 2 (2026): June 2026
Publisher : Academic Bright Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66053/voice.v4i2.792

Abstract

Purpose: This study evaluated an inventory system integrating LSTM forecasting and Hyperledger Fabric blockchain to improve prediction accuracy and transaction integrity. Design/Methods: A design-and-development approach used 12,450 inventory records from Retail Company X (January 2021-December 2023), split chronologically into 70% training, 15% validation, and 15% testing subsets. The LSTM used two hidden layers, 128 units per layer, dropout 0.2, Adam optimizer, learning rate 0.001, batch size 64, and 100 epochs. Blockchain used Hyperledger Fabric with Raft consensus. Evaluation included forecasting benchmarks, 50 stock-modification simulations, and 45 purposively recruited users after hands-on prototype interaction. Findings: LSTM achieved MAE 3.2% and RMSE 4.5%, outperforming Moving Average and Exponential Smoothing. A two-tailed paired-samples t-test across 62 matched testing windows against Exponential Smoothing confirmed significant improvement (t(61) = -5.34, p < 0.001, Cohen's dz = 0.68). Blockchain detected 48 of 50 unauthorized stock modifications, producing a 96% detection rate with two missed detections (4%) and 120 ms latency. User evaluation was positive across forecast accuracy, security, transparency, ease of use, and intention to use. Implications: The prototype can support inventory planning, auditability, and secure transaction records. Originality: The study empirically combines AI forecasting, permissioned blockchain integrity, and user acceptance in one inventory workflow.
Co-Authors - Supriyono Aditia Rasid Adrianus Wayan Dian Adi Pamungkas Ahmad Jazuli Ahmad Jazuli Alvin Rainaldy Hakim Amalia Safitri Amara, Radinda Amelia, Dwi Andy Prasetyo Utomo Annisa Putri Hapsari Anteng Widodo Ardelia Khansa Lathifa Arif Setiawan Arif Setiawan Arifviando, Muhammad Villa Asti Devi Mutiara Khoirun Nisa Aulia Ina Rahma Avin Nuzula Fitranti Bayu Samudro Fadhilah Budi Cahyo Wibowo Budi Gunawan Budi Gunawan Chalim, Noor Dhila Resky Effenti Diana Laily Fithri Diana Laily Fithri Diana Laily Fithri Diana Diana Nur Yasmin Diana, Diana Laily Fithri Dimas Yoga Ardyansyah Djoko Utomo Dwi Septiani Dwi Septiani Dyah Ayu Sukmaningtyas Eko Darmanto Elyza Dewi Fortuna Eviana Hartanti Fadila Ullul Azmie Fajar Nugraha Farel Dani Arfiyan Fatmala, Indah Firdaus, Ricko Muhammad Fitriana Habibullah, Eggy Agusti Hakim, Adam Fathul Herdian Rio Saputro Hidayah, Lisna Hidayat, Muhammad Fariz Azka Hidayatullah, Muhamad Arzak Irawan , Yudie Janah, Susi Nor Jayanti, Septina Dwi Jhany Feronica Ardina Kalya Agil Prasetya Khasan, Nur Akhmad Khoiroh, Zahro Istitho'atul Laily Fithri, Diana Lathifa, Ardelia Khansa Mochammad Imron Awalludin Mohammad Dahlan Muhamad Dimas Firmansyah Muhamad Yuwanda Muhammad Ady Nugroho MUHAMMAD ARIFIN Muhammad Arifin Muhammad Krisza Aditya Muhammad Yusuf Aji Wijaya Mukhamad Nurkamid Noor Latifah Nurul Inayah Nurwijayanti Nurya Herlina Sari Pratiwi Cahyaningtiyas Pratomo Setiaji Putri Kurnia Handayani Putri, Indriani Zabrina Putri, Noor Syafa’ah Kusuma Putri, Sevara Humaira Rahmawati, Yulinda Rasid, Aditia Ratri Rahmawati Reformasiyanto, Mohammad Humam Azka Rizky Muhammad Rizky Maulana Rizkysari Mei Maharani Rizkysari Mei Maharani, Rizkysari Mei Robait Tajuddin Romadhon, Zainur Romadhon, Zainur Rosyada, Mila Santy, Nawal Ari Sari, Luthfiana Semit, Danial Septina Dwi Jayanti Sofian, Ahmad Soni Adiyono Sri Mulyani Sulistiowati Apriliya Eka Wardani Supriyono Supriyono Supriyono Syafiul Muzid Taufiq, Muhammad Bagas Titis Chusnul Mahrom Tri Listyorini Tutik Khotimah Vika Aulia Munawaroh Widiyatmoko, Fahmi Agung Windraningsih Wiwit Agus Triyanto Yudie Irawan Yudie Irawan