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All Journal JURNAL DERIVAT: JURNAL MATEMATIKA DAN PENDIDIKAN MATEMATIKA Infotech Journal Jurnal Informatika Upgris Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Martabe : Jurnal Pengabdian Kepada Masyarakat IJEEIT : International Journal of Electrical Engineering and Information Technology Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Progresif: Jurnal Ilmiah Komputer JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Budimas : Jurnal Pengabdian Masyarakat Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Infotek : Jurnal Informatika dan Teknologi Jurnal Teknik Informatika (JUTIF) Bulletin of Computer Science Research Journal Computer Science and Informatic Systems : J-Cosys International Journal Software Engineering and Computer Science (IJSECS) International Journal of Management Science and Information Technology (IJMSIT) COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Journal of Scientech Research and Development Proceeding of International Conference Health, Science And Technology (ICOHETECH) Mestaka: Jurnal Pengabdian Kepada Masyarakat Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Bengawan :Jurnal Pengabdian Masyarakat Journal Of Artificial Intelligence And Software Engineering CSRID Informasi interaktif : jurnal informatika dan teknologi informasi Jurnal ilmiah teknologi informasi Asia Edu Komputika Journal Governance IT Adoption and Technology Advance
Eko Purwanto
Universitas Duta Bangsa Surakarta
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

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Implementasi Sistem Informasi Pengaduan Siswa Berbasis Web dengan Pemantauan Service Level Agreement Zakhi Febriyan; Sri Surmalinda; Eko Purwanto
Bulletin of Computer Science Research Vol. 6 No. 4 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i4.1180

Abstract

A student complaint system serves as a school service channel allowing students to submit grievances, criticisms, and reports regarding activities and services within the school environment. Complaint management at SMK IPTEK Weru, Sukoharjo Regency, currently lacks digital system support; consequently, report logging is disorganized, resolution processes are time-consuming, and follow-up monitoring is suboptimal. This research aims to design and develop a web-based Student Complaint Information System to provide a more organized and integrated solution for complaint management. The system was built using the Waterfall approach, implemented in stages ranging from requirements identification, design, and application development to functional testing and system maintenance. Research data derived from direct observation, interviews, and literature reviews served as the foundation for system development. The system utilizes Laravel as the application framework and MySQL for data storage. Black-box testing was conducted to ensure that every function operates according to requirements. A key contribution of this research is the implementation of a Service Level Agreement (SLA) feature within the web-based system, enabling the school to monitor the timeliness of complaint resolution in a more structured manner. Results indicate that core features such as login, complaint submission, data management, status monitoring, and report generation function as designed. Meanwhile, User Acceptance Testing (UAT) yielded an acceptance score of 85.00%, placing it in the "Very Good" category. These results demonstrate that the developed system enhances the effectiveness of the complaint process, accelerates report handling, and facilitates complaint management for students, staff, and administrators at SMK IPTEK Weru, Sukoharjo Regency.
Rancang Bangun Sistem Informasi Digitalisasi UMKM Kuliner Berbasis Web sebagai Media Promosi dan Pengelolaan Usaha Fathin Ryfsa Fadilah; Eko Purwanto; Aprilisa Arum Sari
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 2 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i2.34998

Abstract

This research aims to design and develop a web-based information system for culinary MSME merchants to support the digitalization of promotion and business information management in a more effective and integrated manner. The existing problem is that data management and culinary information dissemination are still conducted manually, causing information related to shelters, food and beverage menus, and merchant data to be delivered less optimally to the public. The system development method used in this research is the Waterfall method, which consists of requirement analysis, system design, implementation, testing, and maintenance stages. The system was developed using the Laravel framework and MySQL database, while Unified Modeling Language (UML) was utilized as a system modeling tool. The system provides three access levels, namely admin, seller, and buyer, to support MSME data management, menu management, and centralized culinary information services. This study used 10 shelters as implementation testing data from approximately 120 available shelters. The testing results indicate that the system functions properly and assists users in accessing culinary information more quickly, effectively, and efficiently. The developed web-based system is expected to enhance culinary MSME promotion and support digital transformation in culinary business management.
Comparison of Machine Learning and Deep Learning Algorithms for Daily Retail Sales Forecasting Eko Purwanto; Bangun Prajadi Cipto Utomo; Hanifah Permatasari; Farahwahida Mohd
Edu Komputika Journal Vol. 12 No. 2 (2025): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v12i2.32773

Abstract

This study presents a comparative analysis of four machine learning (ML) and deep learning (DL) algorithms: Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) for predicting daily retail sales time series. The models were evaluated using key metrics, such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²). Results show that RF and SVM outperformed both CNN and LSTM in terms of MAE (3500.28 and 3325.11, respectively) and RMSE (4660.60 and 4293.42, respectively). However, all models had negative R² values, indicating none could explain the variation in the data. LSTM, in particular, was the least efficient model, with an MAE of 54087.25, RMSE of 54257.51, and R² of -158.59. The poor performance of LSTM can be attributed to overfitting, improper model configuration, and misalignment with the nature of the data. The dataset used includes over 1,000 daily retail sales transaction records collected over one year, with key attributes like CustomerID, ProductID, Quantity, Price, TransactionDate, PaymentMethod, StoreLocation, ProductCategory, DiscountApplied, and TotalAmount. While the dataset is representative, its size and complexity may not have been sufficient for deep learning models like LSTM and CNN, which generally require larger datasets for optimal performance. This study highlights the challenges of using deep learning for retail forecasting and suggests future research should focus on refining models and incorporating external datasets to improve prediction accuracy.
Sistem Pendukung Keputusan Pemilihan Calon Penerima Beasiswa dengan Multi Objective Optimization on The Basis of Ratio Analysis Dewi Kuncorowati; Eko Purwanto; Hanifah Permatasari
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6579

Abstract

The selection of scholarship candidates has so far been conducted manually and is not well-documented. The selection process is based on the opinions or personal preferences of the selection team, which can lead to unfairness. There is no consistent standard for evaluating the criteria of scholarship candidates.This study develops a Decision Support System (DSS) for selecting school scholarship candidates using the Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) method. This method is chosen for its ability to handle various qualitative and quantitative evaluation criteria, such as academic achievement, economic conditions, and extracurricular participation. The system is designed to produce candidate rankings objectively and transparently, facilitating fair and accurate decision-making by the school. Testing results indicate that the MOORA-based DSS can provide accurate and consistent recommendations, enhancing the efficiency of the selection process and stakeholder satisfaction. This research also opens opportunities for further development by integrating technologies such as machine learning to enhance system capabilities. The results of this study can assist in determining acceptance of the scholarship
Comparative Analysis of Classification Models for Sales Prediction in E-commerce: Decision Tree, Random Forest, SVM, Naive Bayes, and KNN Purwanto, Eko; Cipto Utomo, Bangun Prajadi; Permatasari, Hanifah; Mohd, Farahwahida
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5224

Abstract

The swift expansion of e-commerce has markedly heightened the necessity for precise sales forecasting, essential for efficient marketing tactics and inventory control. This research evaluates five classification models—Decision Tree, Random Forest, Support Vector Machine (SVM), Naive Bayes, and K-Nearest Neighbors (KNN)—to predict sales outcomes using e-commerce transaction data. The models were assessed utilizing criteria including accuracy, precision, recall, F1-score, AUC, and Log Loss. The findings indicate that Random Forest exceeds the performance of the other models, with an accuracy of 97.5% and an AUC of 0.991, markedly outperforming the alternatives. This study presents a unique contribution by contrasting these classification models in the realm of e-commerce in Indonesia, yielding significant insights for the advancement of more effective predictive algorithms in informatics. The results not only enhance the optimization of marketing strategies but also enrich the comprehension of machine learning applications in sales forecasting. This study underscores the necessity of choosing the appropriate model for enhanced sales forecasting, with considerable ramifications for data-driven decision-making in the e-commerce sector.
Zakat, Trust, and the Digital Window: A Comparative Analysis of Web Disclosure Practices Among Indonesia's National Zakat Institutions Hanifah Permatasari; Liana Trihardianingsih; Eko Purwanto
International Journal of Management Science and Information Technology Vol. 6 No. 2 (2026): July - December 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijmsit.v6i2.7646

Abstract

The National Zakat Amil Institutions (LAZNAS) play a vital role in Indonesia's zakat ecosystem as independent non-governmental organizations connecting muzakki with beneficiaries nationwide, and official websites have become a primary medium through which they communicate identity, programs, and fund management accountability to the public. This study examines digital disclosure patterns across all 15 nationally licensed LAZNAS websites through qualitative content analysis conducted in June 2026, using a five-dimensional rubric adapted from global NPO web disclosure frameworks and enriched with Islamic accountability principles. Findings reveal considerable variation across dimensions and websites, with program reporting the most consistent and financial transparency the most varied. A fund efficiency summary is not yet explicitly available on any website in the sample, reflecting the absence of sectoral norms or regulatory provisions encouraging this type of public digital disclosure, and more than half of the websites face technical barriers that limit public accessibility. The study proposes technical website accessibility as a distinct analytical dimension in Islamic philanthropy web disclosure research and identifies innovative practices with potential for broader sectoral adoption.
SISTEM INFORMASI REKOMENDASI PARIWISATA KABUPATEN SRAGEN MENGGUNAKAN ALGORITMA HYBRID FILTERING Benaya Chessa Sarmanela; Faulinda Ely Nastiti; Eko Purwanto
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

Pencarian informasi pariwisata di Kabupaten Sragen saat ini masih bersifat parsial dan tersebar di berbagai platform, menyulitkan wisatawan dalam merencanakan perjalanan secara terpusat. Penelitian ini bertujuan untuk merancang dan membangun Sistem Informasi Rekomendasi Pariwisata Kabupaten Sragen berbasis website guna memberikan referensi destinasi yang dipersonalisasi. Pengembangan sistem menggunakan metodologi Agile Scrum dengan kerangka kerja Astro untuk frontend dan Supabase sebagai basis data backend. Inti penyelesaian masalah pada sistem ini adalah implementasi algoritma Hybrid Filtering yang mengintegrasikan Content-Based Filtering (CBF) dan Collaborative Filtering (CF). CBF menggunakan metode Term Frequency-Inverse Document Frequency (TF-IDF) dan Cosine Similarity untuk mengekstraksi serta mencocokkan kategori fitur destinasi. Sementara itu, CF menerapkan pendekatan Item-Based dengan korelasi Cosine Similarity untuk menganalisis matriks rating antar pengguna. Penggabungan kedua metode ini menggunakan rasio pembobotan dinamis (basis awal 70:30) yang diarsiteki khusus untuk menanggulangi anomali cold-start pada pengguna baru. Hasil dari perancangan ini adalah sebuah purwarupa sistem komputasi yang mampu mengkalkulasi prediksi kedekatan secara presisi untuk menyajikan daftar peringkat rekomendasi pariwisata sesuai dengan riwayat interaksi pengguna. Kesimpulannya, integrasi Hybrid Filtering dalam pendekatan adaptif Agile Scrum menghasilkan sistem yang terpusat dan berpotensi kuat untuk meningkatkan visibilitas sektor pariwisata lokal.
Implementasi Gaussian Naive Bayes untuk Klasifikasi Permintaan dan Simple Additive Weighting untuk Pemilihan Supplier Pengadaan Barang Arya Kusumadewa; Faulinda Ely Nastiti; Eko Purwanto
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

Proses pengadaan barang memerlukan pengambilan keputusan yang tepat untuk memastikan ketersediaan barang sesuai dengan kebutuhan serta pemilihan supplier yang mampu memenuhi kriteria organisasi. Pengambilan keputusan yang hanya didasarkan pada pengalaman atau pertimbangan subjektif berpotensi menyebabkan ketidaksesuaian jumlah persediaan maupun pemilihan supplier yang kurang optimal. Penelitian ini bertujuan mengimplementasikan algoritma Gaussian Naive Bayes untuk mengklasifikasikan tingkat permintaan barang serta metode Simple Additive Weighting (SAW) untuk menentukan supplier terbaik berdasarkan beberapa kriteria. Dataset yang digunakan merupakan Indonesia E-Commerce Sales and Shipping Dataset 2023-2025 yang diperoleh dari Kaggle dengan jumlah data awal sebanyak 20.848 transaksi. Setelah melalui tahap preprocessing, diperoleh 16.239 data yang digunakan dalam proses klasifikasi. Kategori permintaan dibentuk menjadi tiga kelas, yaitu Low, Medium, dan High, menggunakan metode kuantil (quantile). Model Gaussian Naive Bayes dievaluasi menggunakan confusion matrix dengan metrik accuracy, precision, recall, dan F1-score. Hasil pengujian menunjukkan nilai accuracy sebesar 60,07%, precision 65,95%, recall 60,07%, dan F1-score 57,44%. Selanjutnya, hasil klasifikasi dimanfaatkan sebagai dasar dalam proses pemilihan supplier menggunakan metode SAW berdasarkan kriteria harga, ketepatan waktu pengiriman, kualitas, dan responsivitas. Hasil perhitungan SAW menunjukkan bahwa PT. Mahkota Bisnis memperoleh nilai preferensi tertinggi sebesar 0,9000, sehingga direkomendasikan sebagai supplier terbaik. Hasil penelitian menunjukkan bahwa integrasi Gaussian Naive Bayes dan SAW dapat mendukung proses pengambilan keputusan pengadaan barang secara lebih objektif dan sistematis.
Pendekatan Hybrid SMOTE-Random Forest Untuk Prediksi Risiko Gagal Bayar Fintech Fadhil Ibnu Is’ad; Afu Ichsan Pradana; Eko Purwanto
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

Penilaian risiko kredit yang akurat merupakan instrumen krusial dalam mitigasi risiko finansial pada industri teknologi finansial (FinTech lending). Namun, dataset komersial umumnya mengalami kendala ketidakseimbangan kelas (imbalance data) akibat dominasi debitur lancar, yang menyebabkan algoritma pembelajaran mesin tradisional cenderung bias dan gagal mengidentifikasi debitur berisiko. Penelitian ini bertujuan untuk mengoptimalkan sensitivitas deteksi risiko gagal bayar melalui pendekatan Hybrid SMOTE-Random Forest. Metode penelitian diawali dengan tahap preprocessing berupa pembersihan data dan Min-Max Normalization terhadap dataset Lending Club sebanyak 200 sampel dengan 14 variabel prediktor. Masalah ketidakseimbangan kelas ditangani menggunakan teknik Synthetic Minority Over-sampling Technique (SMOTE) berbasis algoritma k-Nearest Neighbors (k = 5) untuk membangkitkan sampel sintetis pada data latih. Proses klasifikasi dieksekusi menggunakan ensemble pohon keputusan Random Forest dengan skema pembagian data latih dan data uji melalui proporsi percentage split 70:30. Hasil eksperimen riil menunjukkan bahwa integrasi SMOTE berhasil meningkatkan performa model secara drastis dengan menekan angka False Negative menjadi hanya 1 data. Model Hybrid usulan berhasil mencapai metrik performa optimal pada data uji dengan nilai Akurasi global sebesar 95,00%, nilai Presisi sebesar 0,909, nilai Recall (sensitivitas) mencapai 0,952, serta skor harmonik F1-Score sebesar 0,930. Hasil ini membuktikan keandalan pendekatan hybrid sebagai sistem penilaian kredit cerdas yang presisif dan objektif untuk menekan laju kredit macet.
Rancang Sistem Informasi E-Commerce Berbasis Website Terintegrasi Midtrans dan RajaOngkir Pada BUMDes Mojoreno Ghoffar Pangestya Prabowo; Sri Sumarlinda; Eko Purwanto
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

BUMDes Mojoreno memiliki potensi produk lokal yang beragam, namun pengelolaannya masih terkendala oleh sistem pemasaran konvensional, pembukuan manual, serta sistem pembayaran dan perhitungan ongkos kirim yang belum terintegrasi. Hal ini rentan terhadap kesalahan manusia dan memperlambat pelayanan. Penelitian yang dilakukan oleh penulis ini memiliki tujuan merancang sistem informasi e-commerce yang terintegrasi dengan API Midtrans yang digunakan untuk melakukan pembayaran otomatis tanpa konfirmasi ke admin dan API RajaOngkir yang digunakan sebagai perhitungan biaya pengiriman secara waktu nyata. Pengembangan sistem menggunakan metode waterfall dengan batasan pada tahap analisis kebutuhan dan perancangan desain. Analisis kelemahan sistem yang sedang berjalan dievaluasi menggunakan metode PIECES. Perancangan sistem dimodelkan melalui Unified Modeling Language berupa use case diagram yang melibatkan tiga aktor utama dan class diagram dengan delapan entitas, serta rancangan antarmuka pengguna. Hasil penelitian ini adalah sebuah rancangan arsitektur sistem dan antarmuka e-commerce yang siap untuk diimplementasikan ke dalam tahap pengodean menggunakan framework laravel 12. Rancangan ini diharapkan mampu mengatasi kendala operasional dan mengoptimalkan potensi pemasaran produk BUMDes Mojoreno. .
Co-Authors Abdullah Sajad Afu Ichsan Pradana Afu Ichsan Pradana Agus Mardiyono Agustina Srirahayu Ajeng Putri Sulistyawati Akhmad, Khabib Alia Alia Akhmad, Khabib Anggit Nurhidayah Annisa Zalzabilla Rahmawati Aprilisa Arum Sari Ari Zusnan Fahrudin Arya Kusumadewa Atina, Vihi Aulia Fajria Hafidhotun Awong Long, Zalizah Bambang Prasetyo Bangun Pradjadi Cipto Utomo Bangun Prajadi Cipto Utomo Benaya Chessa Sarmanela binti Mohd, Farahwahida Devi Pramita Sari Dewi Kuncorowati Didik Nugroho Djoko Santosa, Tri Dwi Hartanti Dwi Septieni Ema Sagita Desylawati Ery Permana Yudha Fadhil Ibnu Is’ad Farahwahida Mohd Farahwahida Mohd Fathin Ryfsa Fadilah FAULINDA ELY NASTITI Fauzan Sadewa Feny Ramadhani Feri Setiyono Fiqih Dwi Rifai Ghoffar Pangestya Prabowo hanifa Permatasari Hanifah Permatasari, M.Kom Hartanti, Dwi Hartono - Ilham Fahrul Pratama Imam Arifin Indah Nofikasari Indra Hastuti Intan Oktaviani Karima, Yuyun Kris Dayanti Kuswulandari, Reni Liana Trihardianingsih Long, Zalizah Awang Manase Rezata Purba Marginingsih Marginingsih Maulindar, Joni Mohd, Farahwahida Mohd, Farahwahida binti Muhammad Ilham Pratama Niken Galuh Aryani Norma Puspitasari Novita Widyasari Nurchim Nurchim Nurmalitasari Nurmalitasari Nurmalitasari Nurrohman Otami Amalina Pamekas, Bondan Wahyu Permana Yudha, Ery Permatasari, Hanifah Pipin Widyaningsih Prastya, Alvian Bagus Puspita Indah, Ratna Rahmadhani, Istining Ratmini, Yuli Ratna Puspita Indah Rohmat Eko Prasetyo Rudi Susanto, S.Si., M.Si., Ph.D Sabar Sularno Sajad, Abdullah Santosa, Tri Djoko Sari, Hanifah Permata Setiyani, Rahmawati Shandra Isti Kharisma Auliya Alamsyah Sigit Gunawan Singgih Purnomo Softi Ulin Nuha Sopingi Sri Ningrum Sri Rejeki Sri Sumarlinda Sri Sumarlinda, S.Kom, M.Kom, Ph.D Sri Surmalinda Sukma Rahmawati Sundari . Taufiq Nur Arifin Titin Listiani Triyono Triyono Umar Choirul Hadi Velinda Febriana Verdiyanto, Ricky Wiji Lestari Wijiyanto Yusmawan Dwi Suseno Yusuf Esema Zakhi Febriyan