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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 Jurnal SITECH : Sistem Informasi dan Teknologi Jurnal Teknologi Informasi dan Pendidikan 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) Decode: Jurnal Pendidikan Teknologi Informasi Muria Jurnal Layanan Masyarakat Jurnal Locus Penelitian dan Pengabdian Jurnal Teknik Informatika Bhakti Nagori (Jurnal Pengabdian kepada Masyarakat) Jurnal Algoritma Journal of Scientech Research and Development ABDINE Jurnal Pengabdian Masyarakat Jurnal Teknik Indonesia Jurnal Sistem Informasi dan Manajemen Prosiding Seminar Nasional Unimus Media Elektrik Journal of Vocational, Informatics and Computer Education
R Rhoedy Setiawan
Universitas Muria Kudus
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Pendampingan Psikoedukasi dalam Mengatasi Kecanduan Gadget dan Penyalahgunaan Teknologi untuk Kesehatan Mental Masyarakat Jayanti, Septina Dwi; Septiani, Dwi; Lathifa, Ardelia Khansa; Setiawan, R Rhoedy
Muria Jurnal Layanan Masyarakat Vol. 7 No. 1 (2025): Maret 2025
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/mjlm.v7i1.14903

Abstract

This psychoeducation program focuses on raising awareness among adolescent students at SMP N 2 Winong about the negative impacts of gadget addiction and technology misuse, as an essential step in maintaining mental health in the digital era. The goal of this program is to provide students with an understanding of the risks associated with excessive technology use and effective strategies for managing screen time healthily. The methods employed in this program include the delivery of educational content on mental health impacts, interactive discussions, and training in technology management strategies. The results of this program indicate an increase in students' understanding of the importance of balancing technology use with mental health. There was also a positive change in students' behavior regarding screen time management. It is hoped that this program will continue to support efforts to improve the mental health of students at SMP N 2 Winong in a sustainable manner.Kegiatan psikoedukasi ini berfokus pada upaya meningkatkan kesadaran siswa remaja di SMP N 2 Winong mengenai dampak negatif kecanduan gadget dan penyalahgunaan teknologi, sebagai langkah penting dalam menjaga kesehatan mental di era digital. Tujuan dari kegiatan ini adalah memberikan pemahaman kepada siswa tentang risiko penggunaan teknologi yang berlebihan serta cara-cara efektif untuk mengelola waktu layar secara sehat. Metode yang digunakan dalam program ini meliputi penyampaian materi edukatif mengenai dampak kesehatan mental, diskusi interaktif, dan pelatihan strategi pengelolaan teknologi. Hasil dari kegiatan ini menunjukkan peningkatan pemahaman siswa mengenai pentingnya menjaga keseimbangan antara penggunaan teknologi dan kesehatan mental. Terdapat juga perubahan positif dalam perilaku siswa terkait pengelolaan waktu penggunaan gadget. Diharapkan, program ini dapat terus mendukung upaya peningkatan kesehatan mental siswa di SMP N 2 Winong secara berkelanjutan.
Implementasi Algoritma Naive Bayes untuk Klasifikasi Kebutuhan Bahan Baku dalam Upaya Reduksi Food Waste Annisa Putri Hapsari; Eko Darmanto; R. Rhoedy Setiawan
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 17 No. 1 (2026): JURNAL SIMETRIS VOLUME 17 NO 1 TAHUN 2026
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v17i1.16775

Abstract

Inefisiensi dalam manajemen persediaan bahan baku merupakan tantangan krusial bagi bisnis kuliner. Sebelumnya, Santiks Coffee mengalami tingkat food waste sebesar 12,5% akibat metode perencanaan stok manual berbasis intuisi yang berujung pada kelebihan pengadaan bahan mudah rusak dan kejadian stockout. Penelitian ini bertujuan mengembangkan sistem klasifikasi kebutuhan bahan baku berbasis web menggunakan algoritma Naive Bayes Classifier. Model dilatih menggunakan dataset historis penjualan sebanyak 545 rekaman data yang dikumpulkan selama periode september 2025 hingga januari 2026, dengan pembagian data latih dan uji sebesar 80:20. Kebaruan penelitian ini terletak pada integrasi diskritisasi data otomatis berbasis pendekatan statistik kuartil, mengurangi bias subjektivitas dalam penentuan kategori. Hasil evaluasi model menggunakan Confusion Matrix menunjukkan performa yang sangat baik dengan tingkat Akurasi sebesar 93,6%, Presisi 93,8%, Recall 93,6%, dan F1-Score 93,7%. Implementasi sistem untuk prediksi periode Februari 2026 membuktikan bahwa rekomendasi pengadaan berbasis data ini berhasil menekan indikator food waste secara signifikan menjadi 3,7%.
PENGEMBANGAN E-LEARNING WEB UNTUK LAYANAN AKADEMIK SMP NEGERI 1 KLAMBU Muhammad Ady Nugroho; Rhoedy Setiawan
BHAKTI NAGORI (Jurnal Pengabdian kepada Masyarakat) Vol. 6 No. 1 (2026): BHAKTI NAGORI (Jurnal Pengabdian kepada Masyarakat) Juni 2026
Publisher : LPPM UNIKS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/bhakti_nagori.v6i1.5635

Abstract

Kegiatan pengabdian ini dilatarbelakangi oleh penggunaan akses pembelajaran yang masih tersebar di SMP Negeri 1 Klambu Kabupaten Grobogan. Kondisi tersebut membuat guru dan siswa belum memiliki ruang digital bermaksud untuk mengelola materi, tugas, ujian, nilai, dan informasi akademik. Tujuan kegiatan ini adalah mengembangkan sistem e-learning berbasis web sebagai media integrasi layanan pembelajaran dan akademik sekolah. Metode kegiatan meliputi observasi, wawancara, studi pustaka, analisis kebutuhan, perancangan, implementasi, pengujian, dan pendampingan penggunaan sistem. Pengembangan sistem menggunakan model Waterfall karena kebutuhan sistem dapat disusun secara bertahap dan jelas. Hasil kegiatan menunjukkan bahwa sistem telah menyediakan tiga hak akses utama, yaitu admin, guru, dan siswa. Admin dapat mengelola data master, pengguna, mengimpor data, serta pengaturan aplikasi. Guru dapat mengelola mata pelajaran, perangkat, materi, tugas, ujian, nilai, dan profil. Siswa dapat mengakses materi, tugas, evaluasi, nilai, dan profil melalui menu yang lebih terstruktur. Sistem ini memberikan manfaat berupa penyederhanaan akses, keteraturan data akademik, dan peningkatan efisiensi layanan pembelajaran. Kegiatan ini menyimpulkan bahwa e-learning berbasis web dapat menjadi sarana pendukung transformasi sekolah digital, terutama dalam mengintegrasikan layanan akademik dan pembelajaran melalui satu platform.
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.
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.
Sistem Informasi Manajemen Logistik Dan Peralatan Kebencanaan Multi Posko Web-Based Ardianti, Fauzia Alma Eka; Setiawan, Rhoedy; Arifin, Muhammad
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 5 No. 2: JULI 2025
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v5i2.1233

Abstract

Pengelolaan logistik dan peralatan kebencanaan yang efektif menjadi salah satu unsur yang berperan signifikan dalam memperlancar respons terhadap bencana. BPBD Kabupaten Kudus masih menerapkan prosedur non-digital dalam manajemen logistik kebencanaan, yang berisiko menimbulkan keterlambatan dan kesalahan data. Penelitian ini mengembangkan sebuah sistem informasi manajemen logistik dan peralatan kebencanaan multi posko berbasis web guna mempermudah aktivitas logistik kebencanaan mulai pencatatan hingga distribusi secara terpusat. Fitur utama sistem meliputi pencatatan stok, permintaan logistik dari posko, distribusi barang, pemetaan posko bencana, dan pelaporan otomatis. Sistem dibangun menggunakan metode SDLC dengan pendekatan model Rapid Application Development (RAD) guna mempercepat proses pengembangan. Proses perancangan menggunakan UML dan implementasi berbasis PHP serta MySQL. Pengujian black-box menunjukkan sistem berjalan sesuai fungsi. Sistem informasi manajemen logistik yang terintegrasi mampu meningkatkan efisiensi pengelolaan logistik kebencanaan, mengurangi kesalahan pencatatan, dan mempercepat proses distribusi logistik di lingkungan BPBD Kabupaten Kudus.
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.  
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 Ardianti, Fauzia Alma Eka Arif Setiawan Arif Setiawan Arifviando, Muhammad Villa Aulia Ina Rahma Budi Cahyo Wibowo Budi Gunawan Budi Gunawan Chalim, Noor Dhila Resky Effenti Diana Laily Fithri Diana Laily Fithri Diana Laily Fithri Diana Dimas Yoga Ardyansyah Djoko Utomo Dwi Septiani Dyah Ayu Sukmaningtyas Eko Darmanto Elyza Dewi Fortuna Eviana Hartanti Fajar Nugraha Fatmala, Indah Firdaus, Ricko Muhammad Fitriana Habibullah, Eggy Agusti Hakim, Adam Fathul Hidayah, Lisna Hidayat, Muhammad Fariz Azka Hidayatullah, Muhamad Arzak Irawan , Yudie Janah, Susi Nor Jayanti, Septina Dwi Kalya Agil Prasetya Khasan, Nur Akhmad Khoiroh, Zahro Istitho'atul Laily Fithri, Diana Lathifa, Ardelia Khansa Mochammad Imron Awalludin Mohammad Dahlan, Mohammad Muhamad Dimas Firmansyah Muhammad Ady Nugroho MUHAMMAD ARIFIN Muhammad Arifin Muhammad Yusuf Aji Wijaya Mukhamad Nurkamid Noor Latifah Nurul Inayah Nurwijayanti Pratiwi Cahyaningtiyas Pratomo Setiaji Putri Kurnia Handayani 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 Romadhon, Zainur Santy, Nawal Ari Sari, Luthfiana Semit, Danial Sofian, Ahmad Soni Adiyono Sri Mulyani Sulistiowati Apriliya Eka Wardani Supriyono Supriyono Supriyono Syafiul Muzid Taufiq, Muhammad Bagas Tri Listyorini Tutik Khotimah Vika Aulia Munawaroh Widiyatmoko, Fahmi Agung Windraningsih Wiwit Agus Triyanto Yudie Irawan Yudie Irawan Zainur Romadhon