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All Journal Jurnal Ilmiah Kursor TEKMAPRO Journal of Industrial Engineering and Management Jurnal Inotera METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Journal of Information System, Applied, Management, Accounting and Research Jurnal Teknologi Informasi dan Multimedia Jurnal Informatika dan Rekayasa Elektronik International Journal of Economics, Business and Accounting Research (IJEBAR) bit-Tech JTIK (Jurnal Teknik Informatika Kaputama) Jurnal E-Komtek Journal of Intelligent Decision Support System (IDSS) G-Tech : Jurnal Teknologi Terapan Journal of Innovation Information Technology and Application (JINITA) Journal of Applied Data Sciences Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) International Journal Software Engineering and Computer Science (IJSECS) Jurnal Teknik Informatika Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Duta Abdimas: Jurnal Pengabdian Masyarakat Proceeding of International Conference Health, Science And Technology (ICOHETECH) Prosiding Seminar Nasional Teknologi Informasi dan Bisnis SATIN - Sains dan Teknologi Informasi Journal Of Artificial Intelligence And Software Engineering
Wijiyanto
Universitas Duta Bangsa Duta Surakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences

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Website-Based Liquid Selection Recommendation System Using Content-Based Filtering Method at Morevapor Gading Store Rizky Rama Mulyawan; Wijiyanto; Pramono
International Journal Software Engineering and Computer Science (IJSECS) Vol. 4 No. 3 (2024): DECEMBER 2024
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v4i3.3082

Abstract

Liquid is a favorite product among various vape lovers. This product provides a variety of unique and refreshing flavors, attracting the attention of vaping lovers to always try new variants. The high cost of purchasing vape liquid makes many people prefer to buy products recommended according to their preferences, making MoreVapor Gading the main choice. This study aims to develop a recommendation system for selecting vape liquid using a content-based filtering mechanism with the TF-IDF approach. The TF-IDF approach was chosen because of its ability to provide more precise weighting to relevant but not too common words, resulting in more accurate recommendations compared to other methods. In practice, the results of this study provide significant benefits for MoreVapor Gading, namely increasing the accuracy of product recommendations that can minimize ordering errors and increase customer satisfaction and loyalty. This research method uses a waterfall model consisting of the analysis, design, implementation, and testing stages. The results of the study show that from 21 datasets, the system can provide five recommendations with the highest similarity values, namely Cair Grape 0.1445, American Winter Grape Candy Magic 0.1243, Paradewa Grape Athena 0.1151, American Winter Magic Fanta Float 0.0923, and Foom Breeze Series Guava 0.0918 based on user preferences. The recommendation system developed aims to provide accurate recommendations and in accordance with user preferences in choosing vape liquid.
Digitization of Warehouse Stock Management Through Web-Based Information Systems Andreas Adiputra Marpaung; Wijiyanto; Bangun Prijadi Cipto Utomo
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2806

Abstract

Digitalization in warehouse stock management has become increasingly important, particularly for small and medium enterprises that still rely on manual recording methods. These traditional systems often lead to delays, data inaccuracies, and operational inefficiencies. This study aims to design and implement a web-based warehouse stock management information system to improve the recording process, increase accuracy, and support decision-making at Digital Connection, a company still using Microsoft Excel for inventory tracking. The system was developed using the waterfall method, which includes five structured stages: needs analysis, system design, implementation, testing, and maintenance. Functional testing was conducted through black box testing to validate the performance of all system features from a user perspective. The results demonstrate that the developed system enables real-time recording of incoming and outgoing goods, provides interactive data visualization through a dashboard, and issues automatic alerts for minimum stock thresholds. Compared to the previous manual system, the digital solution significantly enhances data accuracy, reduces the risk of duplication or loss, and speeds up reporting processes. This transition not only streamlines warehouse operations but also improves user responsiveness in stock management activities. In conclusion, the proposed information system offers an effective and adaptive approach for small businesses to transition from manual to digital warehouse management, contributing to operational efficiency and supporting broader digital transformation initiatives in logistics and supply chain environments.
Course-Disjoint Evaluation and Capacity-Aware Triage for Student Dropout Risk Prediction Wijiyanto Wijiyanto; Aris Marjuni; Ahmad Zainul Fanani; Ruri Suko Basuki
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1419

Abstract

Early-warning systems for student dropout prevention require evaluation protocols and outputs that remain reliable when applied across heterogeneous academic contexts. This study quantifies how conventional random splits can overestimate performance when models are expected to generalize across different courses and proposes a decision-support layer that translates predicted risk into capacity-aware intervention policies. Using a benchmark higher-education dataset (N=4,424; 34 predictors; three classes: Dropout, Enrolled, Graduate) with 17 Course groups, phased prediction is implemented to reflect increasing evidence availability: S0 (pre-enrollment), S1 (plus semester-1 academic evidence), and S2 (plus semester-2 academic evidence). Baseline results are replicated with leakage-safe preprocessing (imputation, one-hot encoding, scaling) and Synthetic Minority Over-sampling Technique (SMOTE) applied strictly within training folds, comparing multinomial logistic regression, random forest, and tree-based boosting models. Deployment-oriented performance is assessed using StratifiedGroupKFold by Course to enforce course-disjoint testing. Discrimination is reported with Macro-F1 and Balanced Accuracy, while probability quality is evaluated using LogLoss, Brier score, expected calibration error, maximum calibration error, and reliability diagrams. Calibrated probabilities are translated into capacity-aware risk bands (Top-k% triage), selective prediction is evaluated via abstention to defer low-confidence cases, and split conformal prediction sets are optionally reported for multiclass uncertainty communication. Results show consistent performance drops under course-disjoint validation, confirming a non-trivial generalization gap. Error decomposition indicates that Enrolled is the most ambiguous class and exhibits phase-dependent confusion toward both terminal outcomes. Calibration shows phase-specific trade-offs between likelihood-based and worst-case calibration metrics, while risk bands yield high-precision triage under limited capacity, and abstention improves decision quality at reduced coverage. Overall, the study provides a deployment-oriented evaluation and decision-support workflow for translating dropout risk models into actionable capacity planning.
Web-Based Village Public Service Information System in Nambangan Selogiri Village Wonogiri Fahriza Wahyu Akbar; Wijiyanto Wijiyanto; Hanifah Permatasari
Journal of Artificial Intelligence and Software Engineering Vol 6, No 2 (2026): Juni (OnProgress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i2.9500

Abstract

Digital transformation at the village government level has become a crucial aspect of realizing efficient, transparent, and responsive public services. However, Nambangan Village currently faces administrative bottlenecks due to its reliance on a conventional service system. The process of issuing various certificates, such as the Certificate of Inability (SKTM), domicile letters, and cover letters, requires residents to physically visit the village hall. This practice leads to crowded queues, potential errors in population data recording, and low time efficiency in service delivery. This study aims to design and develop a web-based Village Public Service Information System that adapts to the operational needs of both village officials and the community of Nambangan Village. The system development method applied is the Software Development Life Cycle (SDLC) using the Waterfall model, which encompasses the stages of requirements analysis, system design, implementation, testing, and maintenance. The system is built using PHP, HTML, CSS, and JavaScript programming languages, supported by a MySQL database for integrated data management. The primary features implemented include online document submission, population data management, service tracking for village officials, public village information delivery, and a public grievance reporting system. Software quality assurance was functionally evaluated through the Black Box Testing method. The final outcome of this research is expected to provide an applicable technological solution for Nambangan Village to optimize public administrative governance and provide accessible services for the community without spatial or temporal constraints.
PENERAPAN ARTIFICIAL NEURAL NETWORK DALAM DETEKSI SERANGAN PADA WEB SERVER APACHE arif wicahyanto; nurchim nurchim; wijiyanto wijiyanto
Jurnal Informatika dan Rekayasa Elektronik Vol. 8 No. 1 (2025): JIRE APRIL 2025
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/jire.v8i1.1386

Abstract

Serangan siber terhadap website menjadi hal yang tidak dapat dihindarkan, tidak terkecuali website pemerintahan serta website kampus. Serangan siber memberikan dampak yang merugikan, mulai dari pencurian data sensitif, gangguan akses website, hingga kerugian finansial. Seiring dengan semakin canggihnya teknik serangan siber, sistem keamanan berbasis aturan dan pencocokan pola menghadapi kesulitan dalam mendeteksi serangan yang tersembunyi dan adaptif. Artificial Neural Network (ANN) adalah metode pembelajaran mesin yang memiliki kemampuan untuk belajar dari pola serangan yang kompleks, mengidentifikasi pola yang tidak terlihat dan beradaptasi dengan serangan baru. Penelitian ini bertujuan mengimplementasikan ANN dalam bentuk model untuk Smendeteksi serangan siber dengan menggunakan access log web server Apache sumber data dataset. Penelitian berhasil membangun model ANN untuk mendeteksi serangan pada web server Apache dengan nilai accuracy 0.9170.
Machine Learning-Based Diabetes Mellitus Classification Using Multi-Dataset Evaluation and Class Imbalance Resampling Wijiyanto; Agustinus Eko Setiawan; Ferly Ardhy; Ritzkal; Ummi Athiyah
Journal of Innovation Information Technology and Application (JINITA) Vol 8 No 1 (2026): JINITA, June 2026
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v8i1.3315

Abstract

Diabetes mellitus (DM) remains a major global health challenge due to its increasing prevalence and long-term complications, emphasizing the need for accurate early prediction systems. This study proposes a machine learning-based framework for DM classification using a multi-dataset setting while addressing class imbalance issues. Two independent datasets from Iraq and Germany were employed to evaluate model robustness across different population characteristics. The experimental workflow consisted of data preprocessing, stratified train-test splitting, imbalance handling using Synthetic Minority Over-sampling Technique (SMOTE) and SMOTE-Tomek, 10-fold cross-validation, and hyperparameter optimization via GridSearchCV. Four classification algorithms were compared, namely Logistic Regression (LR), K-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM). Experimental results demonstrate that data distribution significantly affects classification performance. Under imbalanced conditions, RF achieved the best performance on the Iraqi dataset with an accuracy of 0.98 and an AUC of 1.00, while KNN and RF reached perfect accuracy (1.00) on the German dataset. After applying SMOTE, all models showed more stable performance, particularly in recall, which reached 1.00, indicating effective minority-class detection. In contrast, SMOTE-Tomek produced only marginal additional improvements. The findings suggest that no single classifier is universally optimal for DM prediction. Instead, model effectiveness depends on dataset characteristics and preprocessing strategies. From a practical perspective, the combination of RF and SMOTE shows strong potential for early diabetes screening and clinical decision-support systems. Further validation using larger and more heterogeneous external datasets is recommended.
Pemberdayaan Guru Sekolah Menengah Pertama Melalui Pedagogi Digital Berbasis Interactive Flat Panel Wijiyanto Wijiyanto; Nurchim
Duta Abdimas Vol. 5 No. 1 (2026): Duta Abdimas: Jurnal Pengabdian Masyarakat
Publisher : LPPM Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/6menp933

Abstract

Perkembangan pembelajaran abad ke-21 menuntut guru tidak hanya menguasai perangkat teknologi, tetapi juga mampu menggunakannya secara pedagogis dan bermakna. Pada praktiknya, pemanfaatan teknologi di sekolah masih sering berfokus pada aspek teknis, sementara penguatan strategi pembelajaran belum menjadi perhatian utama. Kondisi ini mendorong perlunya program pendampingan yang realistis, kontekstual, dan berkelanjutan. Kegiatan pengabdian kepada masyarakat ini bertujuan memberdayakan guru lintas mata pelajaran di SMP Tamirul Islam Surakarta melalui penguatan pedagogi digital berbasis Interactive Flat Panel (IFP). Metode yang digunakan adalah pendampingan berbasis komunitas yang dilaksanakan melalui pengembangan materi pembelajaran interaktif dan penerapan langsung IFP dalam kegiatan belajar mengajar. Guru terlibat aktif dalam perencanaan, pelaksanaan, serta refleksi pembelajaran, dengan dukungan teknis untuk memastikan kelancaran implementasi. Hasil kegiatan menunjukkan bahwa pemanfaatan IFP mendorong perubahan praktik pembelajaran menuju pola yang lebih interaktif, kolaboratif, dan berorientasi pada pembelajaran aktif. Guru menunjukkan peningkatan kepercayaan diri dalam mengintegrasikan teknologi serta pergeseran peran dari penyampai materi menjadi fasilitator pembelajaran. Temuan ini menegaskan bahwa teknologi pembelajaran akan memberikan dampak optimal apabila diintegrasikan secara seimbang dengan tujuan pembelajaran dan strategi pedagogik. Program ini berpotensi dikembangkan sebagai model pengembangan profesional guru yang kontekstual dan berkelanjutan.
Analisis Sentimen Pengguna Aplikasi ChatGPT Berdasarkan Ulasan Google Play Store Menggunakan Metode TF-IDF dan Support Vector Machine (SVM) Rangga Yudha Syahbana; Wijiyanto; Agustina Srirahayu
Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Prosiding Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB) 2026
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

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

Abstract

Perkembangan teknologi kecerdasan buatan generatif seperti ChatGPT telah meningkatkan penggunaan aplikasi berbasis Artificial Intelligence (AI) dalam berbagai bidang, seperti pendidikan, pekerjaan, dan pencarian informasi. Meningkatnya jumlah pengguna menyebabkan munculnya berbagai ulasan yang berisi opini, pengalaman, serta tingkat kepuasan pengguna terhadap aplikasi tersebut. Oleh karena itu, diperlukan analisis sentimen untuk mengetahui kecenderungan opini pengguna berdasarkan ulasan yang diberikan. Penelitian ini bertujuan untuk membangun sistem analisis sentimen terhadap ulasan pengguna aplikasi ChatGPT menggunakan metode Term Frequency-Inverse Document Frequency (TF-IDF) sebagai metode ekstraksi fitur dan Support Vector Machine (SVM) sebagai metode klasifikasi. Dataset yang digunakan berupa 5.000 ulasan pengguna aplikasi ChatGPT yang diperoleh dari Google Play Store. Tahapan penelitian meliputi pengumpulan dataset, pelabelan sentimen berdasarkan rating pengguna, preprocessing teks, pembobotan fitur menggunakan TF-IDF, klasifikasi menggunakan algoritma SVM, serta evaluasi model menggunakan Accuracy, Precision, Recall, dan F1-Score. Hasil pengujian menunjukkan bahwa kombinasi metode TF-IDF dan SVM menghasilkan nilai Accuracy sebesar 90,10%, Precision sebesar 87,19%, Recall sebesar 90,10%, dan F1-Score sebesar 88,38%. Sistem yang dikembangkan berhasil mengklasifikasikan ulasan pengguna ke dalam tiga kategori sentimen, yaitu positif, negatif, dan netral, sehingga dapat membantu memahami persepsi pengguna terhadap aplikasi ChatGPT berdasarkan ulasan yang diberikan.
Sistem Informasi Penyewaan Alat Outdoor Berbasis Website Di Desa Wisata Paranggupito Dwi Murdiati; Wijiyanto -; Agustina Srirahayu
Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Prosiding Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB) 2026
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

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

Abstract

Perkembangan teknologi informasi mendorong digitalisasi layanan penyewaan, termasuk pada pengelolaan penyewaan alat outdoor di Desa Wisata Paranggupito yang sebelumnya masih dilakukan secara konvensional. Proses tersebut menimbulkan berbagai kendala, seperti pencatatan transaksi yang kurang efisien, proses pembayaran yang masih manual, serta belum tersedianya informasi pendukung bagi penyewa dalam menentukan perlengkapan yang sesuai dengan kondisi cuaca. Penelitian ini bertujuan mengembangkan Sistem Informasi Penyewaan Alat Outdoor berbasis website yang mampu meningkatkan efektivitas pengelolaan penyewaan sekaligus memberikan kemudahan bagi pengguna. Metode pengembangan yang digunakan adalah Waterfall, yang meliputi tahapan analisis kebutuhan, perancangan sistem, implementasi, pengujian, serta pemeliharaan. Sistem dibangun menggunakan fitur katalog alat, booking online, pembayaran digital melalui Midtrans, informasi cuaca yang terintegrasi dengan OpenWeatherMap API, rekomendasi alat berdasarkan kondisi cuaca, serta QR Code sebagai kode booking untuk proses pengambilan alat. Hasil penelitian menunjukkan bahwa sistem mampu mempermudah proses penyewaan secara daring, mempercepat transaksi pembayaran, meningkatkan akurasi pengelolaan data, serta membantu penyewa dalam memilih perlengkapan yang sesuai dengan kondisi cuaca. Dengan demikian, sistem yang dikembangkan dapat mendukung layanan penyewaan yang lebih efektif, efisien, modern, dan terintegrasi sehingga mampu meningkatkan kualitas pelayanan di Desa Wisata Paranggupito.
Sistem Informasi Manajemen Persediaan Bahan Baku Menggunakan Metode FIFO Berbasis Web Dzakaria Azizi; Wijiyanto -; Hanifah Permatasari
Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Prosiding Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB) 2026
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

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Abstract

Manajemen persediaan bahan baku di CV. Bengawan Jaya Abadi saat ini masih berjalan manual menggunakan buku fisik dan Microsoft Excel. Hal ini menyulitkan pelacakan stok secara real-time, memicu kesalahan pencatatan, dan meningkatkan risiko bahan baku kedaluwarsa akibat penumpukan stok lama. Penelitian ini bertujuan membangun sistem informasi manajemen persediaan bahan baku berbasis web dengan metode First In First Out (FIFO). Kehadiran sistem ini dirancang untuk mengotomatisasi pengelolaan data master, transaksi logistik, validasi Quality Control (QC), dan pengeluaran barang. Sistem dikembangkan menggunakan metode Waterfall dan pemodelan UML, dengan dukungan teknologi PHP, MySQL, serta framework Laravel dan Filament. Hasil pengujian melalui Black Box Testing dan User Acceptance Testing (UAT) membuktikan bahwa sistem berfungsi valid dan meraih predikat sangat layak. Implementasi sistem ini terbukti efektif dalam mengoptimalkan efisiensi pengelolaan persediaan bahan baku di CV. Bengawan Jaya Abadi.
Co-Authors Afu Ichsan Pradana Agus Suyatno Agustina Srirahayu Agustina Srirahayu Ahmad Zainul Fanani Amilia Ayu Lala Kusumaningtyas Andreas Adiputra Marpaung arif wicahyanto Aris Marjuni Bangun Prijadi Cipto Utomo Dinda Saraswati Dwi Murdiati Dzakaria Azizi Eko Pranoto Saputro Eko Purwanto Eko Setiawan, Agustinus Ety Meikhati Fachriyan Yoga Wibawa Fahriza Wahyu Akbar Fajar Saputra Ferly Ardhy Hasanah, Herliyani Indra Hastuti Irawan, Etwin Hendri Maulindar, Joni Mink Poo Lexy Utomo Mink Poo Lexy Utomo Moh Muhtarom Muhammad Khovivul Anam Mulyawan, Rizky Rama Nughthoh Arfawi Kurdhi, Nughthoh Arfawi Nugroho, Ledjar Nugroho Nurchim Nurohman Pegi Hasyim Rosidi Permatasari, Hanifah Pramono Pramono Primus Ramadon Rangga Yudha Syahbana Ritzkal, Ritzkal Rizky Rama Mulyawan Rizqi Saputra Ruri Suko Basuki Saefudin, Mochamad Sopingi Sopingi SRI SUMARLINDA Sri Widodo Titi Jayanti Ummi Athiyah Vihi Atina Wayan Hany Robbayani Wiji Lestari Wiji Lestari Yoga Prasetiawan, Ardhian Yudika Novianto Yuwono, Adhi