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All Journal Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Sinkron : Jurnal dan Penelitian Teknik Informatika CogITo Smart Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Journal of Education Technology Jurnal Tekno Insentif JURTEKSI Building of Informatics, Technology and Science Infotekmesin Journal of Information Systems and Informatics Jurnal Sistem Komputer dan Informatika (JSON) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia Jurnal Pengabdian Masyarakat Indonesia Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Jurnal Pengabdian Mitra Masyarakat (JPMM) Electronic Journal of Education, Social Economics and Technology Bulletin of Social Informatics Theory and Application Jurnal Riset Teknik Komputer CSRID Southeast Asia Journal on Open and Distance Learning
Rujianto Eko Saputro
Universitas Amikom Purwokerto
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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PENERIMAAN PLATFORM QUIZIZZ DALAM PEMBELAJARAN INFORMATIKA: INTEGRASI TECHNOLOGY ACCEPTANCE MODEL (TAM) DAN INTRINSIC MOTIVATION INVENTORY (IMI) PADA SMPN 3 SUSUKAN BANJARNEGARA Titi Safitri Maharani; Rujianto Eko Saputro
Jurnal Riset Teknik Komputer Vol. 3 No. 1 (2026): Maret : Jurnal Riset Teknik Komputer (JURTIKOM)
Publisher : CV. Denasya Smart Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69714/fr8t0r75

Abstract

The use of technology in education has rapidly developed, particularly in assessment methods. This study aims to analyze the acceptance of Quizizz in learning by applying the Technology Acceptance Model (TAM) and the Intrinsic Motivation Inventory (IMI) approaches. Data were collected from 222 respondents at SMPN 3 Susukan who actively used Quizizz and were analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method. The analysis results show that in the TAM model, Attitude Toward Using (AM) had the strongest influence on Behavioral Intention (BI) (β = 0.744; p < 0.001), while Self-Efficacy (SE) and Technology Facilitating Conditions (TF) significantly influenced Perceived Ease of Use (PEU). However, Perceived Usefulness (PU) and PEU did not have a significant effect on BI. Meanwhile, the IMI model showed that intrinsic motivation has not formed a strong structural pattern in explaining technology acceptance. The study concludes that cognitive-perceptual factors are more dominant than affective-motivational factors in influencing acceptance.
Comparative Performance Analysis of Random Forest and Logistic Regression for Sentiment Classification of the Makan Bergizi Gratis Program on Platform X Prianto, Slamet Endro; Berlilana, Berlilana; Saputro, Rujianto Eko
Journal of Information System and Informatics Vol 8 No 1 (2026): February
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i1.1371

Abstract

The rapid growth of e-commerce has made personalized product recommendations a crucial aspect of enhancing customer satisfaction and boosting sales. However, many small-to-medium-sized retail businesses, like Adiva Fashion Store, still rely on manual product selection through customer searches or seller recommendations, which often leads to challenges in meeting customer preferences. This study presents a case study of Adiva Fashion Store, where the Collaborative Filtering method was implemented to develop a personalized clothing product recommendation system. The item-based Collaborative Filtering approach was employed to calculate the similarity between products based on customer ratings and transaction history. These similarity values were then used to predict customer preferences for products that had not yet been purchased. The system was developed using the Waterfall methodology, which involved needs analysis, system design, implementation, testing, and maintenance. The results show that the recommendation system significantly improved the relevance of product suggestions, helping customers make better purchasing decisions and increasing sales effectiveness. This case study illustrates how data-driven recommendation systems can be effectively integrated into small-to-medium-sized retail environments, providing valuable insights for other businesses aiming to adopt similar strategies.
Comparison of the Accuracy Levels of Naive Bayes, Random Forest, and Long Short-Term Memory (LSTM) Methods in Predicting Gold Jewelry Sales Pandu W, Muhammad Arfianto; Saputro, Rujianto Eko; Purwadi, Purwadi; Rohmah, Umdah Aulia
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5139

Abstract

Gold has long been recognized as a safe haven asset, especially during economic uncertainty. Accurate prediction of gold jewelry sales is essential for inventory management and business strategy, particularly in high-demand regions such as Imogiri. This study aims to compare the accuracy levels of three machine learning methods—Naïve Bayes, Random Forest, and Long Short-Term Memory (LSTM)—in predicting gold jewelry sales using historical transaction data from Toko Emas Parimas. The dataset comprises 4,595 records from January 2022 to December 2024. The research employs data preprocessing, including data cleaning, feature transformation, and normalization, followed by classification into sales categories. Two data-splitting schemes (80:20 and 70:30) were implemented to evaluate model generalization. The models were trained and tested using performance metrics such as accuracy, precision, recall, and F1-score. The results show that Random Forest achieved perfect classification with an accuracy of 1.00 in both schemes, outperforming the other models. Naïve Bayes also performed well with accuracy up to 0.98, while LSTM showed moderate results with accuracy ranging from 0.82 to 0.88. These findings indicate that Random Forest is the most reliable model for sales prediction of gold jewelry, especially for static classification tasks. The study provides practical insights for retailers and decision-makers in selecting suitable analytical models, and it highlights the importance of aligning analytical methods with data characteristics to improve decision support systems in retail.
Adaptive Test Model Enhancement Based on Salmon Salar Optimization and Partially Observable Markov Decision Process Saputro, Rujianto Eko; Utomo, Fandy Setyo; Wanti, Linda Perdana
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1065

Abstract

Cognitive Diagnosis Models (CDMs) in Computerized Adaptive Testing (CAT) are widely used to assess students’ cognitive abilities; however, existing approaches face significant limitations. The Latent Trait Model often suffers from specification errors due to its complexity, the Diagnostic Classification Model encounters difficulties in integrating hierarchical structures, and Deep Learning Models demand substantial computational resources. To address these challenges, this study introduces Salmon Salar Optimization (SSO) to enhance CDM performance and integrates the Partially Observable Markov Decision Process (POMDP) to improve dynamic question selection. The proposed adaptive testing framework comprises three components: preprocessing, CDM, and a selection algorithm. Experimental results on the ASSISTments 2009-2010 dataset demonstrate that SSO outperforms representative baselines from both deep learning: Neural CD and Latent Trait Model: MIRT approaches. Using 5-fold cross-validation, the proposed model achieved superior predictive performance with 75.51% accuracy and an AUC of 0.8191, highlighting its robustness compared to existing state-of-the-art methods. Furthermore, adaptive test simulations reveal that the SSO- and POMDP-based model delivers superior outcomes, attaining 80.3% accuracy with a reward of 8.03 for 10-question exams and 79.8% accuracy with a reward of 11.97 for 15-question exams. These findings confirm the effectiveness of the proposed model in enhancing cognitive diagnosis and adaptive testing performance.
Pelatihan dan Penerapan Internet of Things untuk Pengeringan Kopi di MTs Pakis, Dusun Pesawahan, Desa Gununglurah, Kecamatan Cilongok, Kabupaten Banyumas, Provinsi Jawa Tengah: Instalasi, Pendampingan, dan Evaluasi Dampak Saputro, Rujianto Eko; Darso, Darso; Hariyanti, Anies Indah; Putri, Qeisha Amaliya; Salsabila, Sabita; Damaito, Aditya Hanif Hadian; Qolbu, Aufiatu Risqiyah Nur Ainun; Mukti, Gilang Deli; Fauzan, Akhmad
Jurnal Pengabdian Masyarakat Indonesia Vol 6 No 1 (2026): JPMI - Februari 2026
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpmi.4333

Abstract

Kopi "Igir Alas" yang dikelola oleh MTs Pakis menghadapi tantangan utama berupa rendahnya efisiensi dan inkonsistensi kualitas biji kopi akibat proses pengeringan konvensional yang lama dan rentan terhadap fluktuasi cuaca. Program pengabdian masyarakat ini bertujuan meningkatkan kompetensi sumber daya manusia (SDM) dan mengimplementasikan sistem monitoring cerdas berbasis Internet of Things (IoT), didukung oleh perbaikan infrastruktur dry house dan standar K3. Pelatihan yang melibatkan 31 peserta (30 siswa dan 1 pengelola) menunjukkan keberhasilan yang signifikan: peningkatan skor pemahaman peserta dari 30,00 menjadi 82,00, menghasilkan Normalized Gain (<g>) sebesar 0,74 (efektivitas tinggi) dan Peningkatan Persentase 173%. Secara operasional, sistem IoT berhasil mempersingkat durasi pengeringan dari 7–8 hari menjadi 4–5 hari, meningkatkan kapasitas hingga 100%, dan menstabilkan kadar air. Keberlanjutan program didukung oleh penghematan jam kerja, kemandirian pemantauan sistem, dan rencana tindak lanjut kritis seperti pengembangan Bank Data Kadar Air dan instalasi flow meter untuk menjaga kualitas produk dan efisiensi operasional.
Performance Comparison Of Xgboost Lightgbm And Lstm For E-Commerce Repeat Buyer Prediction Nugroho, Lustiyono Prasetyo; Saputro, Rujianto Eko; Utomo, Fandy Setyo
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5746

Abstract

Repeat buyer behavior is a critical indicator of customer retention success in e-commerce platforms. However, accurately predicting repeat buyers remains a challenging problem due to the complexity of user behavior patterns and the temporal characteristics embedded in interaction data. Existing studies often focus on single modeling approaches or limited sequence exploration, resulting in insufficient comparative insight between ensemble-based machine learning and sequence-based deep learning models. Therefore, this study aims to systematically compare the performance of tree-based ensemble models (XGBoost and LightGBM) and a sequence-based deep learning model (LSTM) in predicting repeat buyers using user behavior data. To ensure fair evaluation, data preprocessing and feature engineering were carefully designed to prevent data leakage by utilizing user behavior prior to the first purchase. Model performance was evaluated using Accuracy, F1-score, and ROC–AUC metrics. Experimental results show that XGBoost and LightGBM achieve stable classification performance with accuracy values of 86.11% and 85.84%, respectively, while the LSTM model attains the highest ROC–AUC value of 0.937, indicating superior capability in capturing temporal behavioral patterns. This study provides valuable insights for e-commerce platforms seeking to optimize predictive models for repeat buyers, contributing to more effective customer retention strategies.
Co-Authors Adam Prayogo Kuncoro Adam Prayogo Kuncoro Adiya, Az Zahra Dwi Nur Afriansyah, Fery Aimah, Samsul Akhmad Fauzan Arif Mu'amar Wahid Aulia Hamdi Azhari Shouni Barkah Bagaskoro, Galih Berlilana Berlilana Cahyo, Samsul Dwi Chyntia Raras Ajeng Widiawati Damaito, Aditya Hanif Hadian Damayanti, Wenti Risma Dani Arifudin Darmono Darso, Darso Deasy Komarasary Dhanar Intan Surya Saputra Dhanar Intan Surya Saputra Ely Purnawati Ely Purnawati, Ely Embong Octavianto Fandy Setyo Utomo Fatudin, Arif Faturama, Rafi Febriansyah Husni Adiatma Febrianti, Diah Ratna Fery Afriansyah Giat Karyono Hariyanti, Anies Indah Hasna Salsa Dhia hidayatulloh, hanif Ikmah Ikmah Ikmah, Ikmah Ilham, Rifqi Arifin Indriyani, Ria Irwansyah Munandar Ismail, Dimas Shafa Malik Junianto, Haris Kusuma, Bagus Adhi Latif, Imam Sofarudin Lughri Wijaya Pamungkas Maharani, Revalyna Octavia Maulana Baihaqi, Wiga Millatul Izza, Nia Mohd. Hafiz Zakaria Mukti, Gilang Deli Munandar, Irwansyah Nanjar, Agi Ndari, Arum Vika Nia Millatul Izza Novita Eka Ramadhani Nugroho, Lustiyono Prasetyo Nurfaizi, Maulana Nurmalitasari, Gupita Octavianto, Embong Pandu W, Muhammad Arfianto Prasetyo, Agung Prianto, Slamet Endro Pungkas Subarkah Purwadi Purwadi Purwadi Purwadi Putri, Qeisha Amaliya Qolbu, Aufiatu Risqiyah Nur Ainun R. Vitto Mahendra Putranto Radeta Tea Makdatuang Ramadhan, Rio Fadly Ria Indriyani Rizqi Aulia Widianto Rohmah, Umdah Aulia Rosana Fadila Sari safitri feriawan, Titi Salam, Sazilah Salsa Dhia, Hasna Salsabila, Sabita Samsul Aimah Saputra , Dhanar Intan Surya Saputra, Alfin Nur Aziz Saputri, Inka Sari, Rida Purnama Sarmini Sarmini - Sarmini Sarmini Sarmini Sazilah Salam Serli, Serli Shendy Filanzi Sofa, Nur Sri Hartini Suliswaningsih, Suliswaningsih Syahputra, Akhmal Angga Tanzilla, Armeyta Putri Tarwoto, T Tea Makdatuang, Radeta Titi Safitri Maharani Toni Anwar Turino, Turino Wahyuni, Irmawati Tri Wanti, Linda Perdana Wenti Risma Damayanti Wiga Maulana Baihaqi Wijaya, Anugerah Bagus Yuli Purwati Yulianto, Koko Edy