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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Sebatik JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) J-SAKTI (Jurnal Sains Komputer dan Informatika) IJISTECH (International Journal Of Information System & Technology) JURTEKSI MIND (Multimedia Artificial Intelligent Networking Database) Journal Aptisi Transactions on Technopreneurship (ATT) METIK JURNAL JURNAL IT: Media Infromasi STMIK Handayani Makassar Building of Informatics, Technology and Science INFOKUM Brahmana : Jurnal Penerapan Kecerdasan Buatan TEPIAN IJISTECH Buletin Poltanesa J-SAKTI (Jurnal Sains Komputer dan Informatika) Informatika Wicida Jurnal Teknik Informatika Journal of Artificial Intelligence and Engineering Applications (JAIEA) Journal of Pedagogy and Education Science Journal of Informatics and Electronics Engineering TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi BEduManageRs Journal : Borneo Educational Management and Research Journal Information Technology Studies Journal Research in Education, Technology, and Multiculture
Heny Pratiwi
STMIK Widya Cipta Dharma
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Social Sciences Other

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Penerapan Data Mining Dalam Menganalisis Pola Belanja Konsumen Menggunakan Market Basket Analysis Sarifmata Purnomo; Heny Pratiwi; Sa'ad, Muhammad Ibnu
METIK JURNAL (AKREDITASI SINTA 3) Vol. 7 No. 2 (2023): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v7i2.678

Abstract

Currently, almost every activity is related to data. in the business sector, daily sales transaction data stored in the database system will always increase and accumulate. The existing data is only used as an archive by the shop owner so that it has an impact on sales strategies that are not implemented well, even though the existing data can be processed into information to determine the layout of goods so that it has an impact on increasing the occurrence of impulse buying, increasing or maintaining turnover, and minimizing product waste. accumulate until it expires which can be detrimental to the shop.The aim of this research is to find consumer shopping patterns using Marker Basket Analysis. This research method is called market basket analysis or also called association rules, which is a data mining technique for finding patterns that often appear simultaneously in transaction data, so that it can be used as a method for finding information about what kinds of goods are frequently used. purchased by consumers simultaneously. The results of this research, based on data analysis using the Rapidminer application, found 25 associative relationships or rules with a lift ratio value of more than 1, these rules become a reference in determining the layout of goods. Providing recommendations for layout changes aims to make it easier for consumers to shop, increase the possibility of impulse buying by consumers, and maximize product display, thereby reducing the accumulation of goods in the Purnama Store Warehouse.
Strategi Manajemen Pendidikan Berbasis Machine Learning untuk Prediksi Prestasi Siswa Pratiwi, Heny; Sa'ad, Muhammad Ibnu; Salmon
BEduManagers Journal : Borneo Educational Management and Research Journal Vol. 6 No. 1 (2025): BEduManagers Journal : Borneo Educational Management and Research Journal
Publisher : Manajemen Pendidikan Program Doktor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/bedu.v6i1.5016

Abstract

Prediksi prestasi akademik siswa berbasis data menjadi keperluan strategis dalam manajemen pendidikan modern. Studi ini mengkaji efektivitas dua model Machine Learning—Support Vector Machine (SVM) dan Random Forest—dalam memprediksi capaian akademik peserta didik SMA Negeri menggunakan data sintetis yang menyerupai data riil sekolah. Dataset dikembangkan dari tiga variabel utama: nilai semester, tingkat kehadiran, dan latar belakang sosial ekonomi. Model diuji menggunakan validasi silang lima lipat dan dievaluasi melalui metrik akurasi, presisi, recall, serta F1-score. Hasil menunjukkan bahwa Random Forest lebih stabil dan unggul secara akurasi dibandingkan SVM dalam konteks data multidimensi non-linier. Studi ini menunjukkan potensi integrasi sistem prediktif ke dalam praktik manajerial sekolah untuk mendukung pengambilan keputusan berbasis data yang lebih akurat dan preventif terhadap kegagalan akademik.
Optimalisasi Manajemen Pendidikan Melalui Penerapan Kecerdasan Buatan untuk Meningkatkan Efektivitas Pengambilan Keputusan Pratiwi, Heny; Sa'ad, Muhammad Ibnu; Dovist Calvino
BEduManagers Journal : Borneo Educational Management and Research Journal Vol. 6 No. 1 (2025): BEduManagers Journal : Borneo Educational Management and Research Journal
Publisher : Manajemen Pendidikan Program Doktor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/bedu.v6i1.5025

Abstract

Kemajuan teknologi digital saat ini membuka peluang besar dalam transformasi dan inovasi manajemen pendidikan. Penelitian ini bertujuan mengembangkan dan menguji sistem pendukung keputusan berbasis kecerdasan buatan yang mampu menganalisis dan mengolah data akademik serta administratif secara real-time untuk meningkatkan efektivitas dan efisiensi pengambilan keputusan di institusi pendidikan. Data yang dianalisis meliputi kinerja akademik, tingkat kehadiran, serta informasi administratif siswa. Metode penelitian menggunakan validasi silang lima lipat untuk menguji performa sistem berdasarkan kecepatan pengambilan keputusan dan akurasi prediksi masalah akademik. Hasil penelitian menunjukkan adanya peningkatan kecepatan pengambilan keputusan hingga 30% dan akurasi prediksi mencapai 85%. Temuan ini menegaskan bahwa penerapan teknologi kecerdasan buatan dapat mempercepat proses pengambilan keputusan sekaligus meningkatkan ketepatan strategi manajemen pendidikan, sehingga mendukung terciptanya sistem pendidikan yang lebih adaptif, responsif, dan berkualitas.
Perancangan Model Sistem Aspirasi Mahasiswa Berbasis Web Menggunakan Metode Rapid Application Development Di STMIK Widya Cipta Dharma Cembes, Yosefina; Ekawati, Hanifah; Pratiwi, Heny
JEKIN - Jurnal Teknik Informatika Vol. 5 No. 2 (2025)
Publisher : Yayasan Rahmatan Fidunya Wal Akhirah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58794/jekin.v5i2.1439

Abstract

Aspirasi mahasiswa merupakan bentuk partisipasi aktif dalam menyampaikan pendapat, kritik, dan saran terhadap sistem yang berlaku di lingkungan perguruan tinggi. Namun, mekanisme penyampaian aspirasi di STMIK Widya Cipta Dharma masih menggunakan Google Form yang belum terintegrasi, sehingga menimbulkan kendala dalam efisiensi, dokumentasi, dan transparansi pengelolaan. Penelitian ini bertujuan untuk merancang sistem aspirasi mahasiswa berbasis web sebagai solusi yang lebih terstruktur dan efektif. Pengembangan dilakukan menggunakan metode Rapid Application Development (RAD), yang memungkinkan proses iteratif dan melibatkan umpan balik langsung dari pengguna. Sistem ini dirancang untuk tiga jenis pengguna utama: mahasiswa, admin, dan penanggung jawab institusi. Salah satu kebaruan (novelty) sistem ini dibandingkan penelitian sebelumnya adalah adanya fitur pengajuan aspirasi secara anonim tanpa login serta pelacakan status aspirasi secara real-time. Selain itu, penelitian ini juga menambahkan evaluasi usability menggunakan System Usability Scale (SUS), yang belum banyak diterapkan pada penelitian serupa sebelumnya. Hasil pengujian blackbox menunjukkan bahwa seluruh fitur berfungsi dengan baik, dan evaluasi SUS terhadap 10 responden menghasilkan skor rata-rata 83,3, yang termasuk kategori Excellent. Secara praktis, sistem ini terbukti meningkatkan efisiensi, aksesibilitas, dan transparansi dalam pengelolaan aspirasi mahasiswa, serta memberikan pengalaman pengguna yang positif. Kontribusi utama dari penelitian ini adalah terciptanya sistem awal yang adaptif, aman, dan responsif terhadap kebutuhan mahasiswa, serta dapat dijadikan landasan untuk pengembangan sistem layanan aspirasi digital yang lebih komprehensif di masa mendatang.
Studi Perbandingan Metode MABAC dan WASPAS dengan Pembobotan ROC dalam Sistem Pendukung Keputusan Pemilihan Supplier Terbaik Pratiwi, Heny; Sa’ad, Muhammad Ibnu; Hasiholan, Jundro Daud
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7278

Abstract

The selection of the right supplier is a crucial factor in the supply chain to ensure product quality, cost efficiency, and timely delivery. This study aims to determine the best supplier by comparing two multi-criteria decision-making methods: Multi-Attributive Border Approximation Area Comparison (MABAC) and Weighted Aggregated Sum Product Assessment (WASPAS). Five key criteria were used in the evaluation: product quality, price, delivery punctuality, service and responsiveness, and reputation and trust. The analysis results show that PT. Indo Makmur (A1) consistently ranked first in both methods, with the highest scores of 0.456 (MABAC) and 0.982 (WASPAS), making it the recommended supplier. PT. Sukses Bersama (A7) and PT. Cahaya Abadi (A3) ranked second and third in both methods, indicating good performance. Meanwhile, UD. Sentosa Jaya (A4) ranked the lowest in both methods, suggesting that this supplier is less competitive than the other alternatives. The comparison of results between MABAC and WASPAS methods demonstrates ranking consistency, confirming that both methods can be reliably used in decision-making. This study provides data-driven recommendations for companies in selecting the best supplier, thereby enhancing supply chain efficiency and supporting long-term business strategies.
Analisis Sentimen Orang Tua Murid Baru Terhadap SMPN 40 Samarinda pada SPMB 2025 Menggunakan Algoritma Naïve Bayes Ananta Putra, Resifa; Heny Pratiwi; Ahmad Abul Khair
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

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

Abstract

The New Student Admission Selection (SPMB) plays an essential role in ensuring equal educational access in Indonesia. However, during SPMB 2025 at SMPN 40 Samarinda, many candidates living nearby did not choose the school as their first preference, suggesting that perceptions and school image significantly influenced their choices. This study aims to analyze new student parents' sentiments toward SMPN 40 Samarinda using the Naïve Bayes algorithm combined with the Term Frequency–Inverse Document Frequency (TF-IDF) technique. Data were collected from 42 respondents and categorized into positive, neutral, and negative sentiments. The model achieved an accuracy of 86%, a precision of 56%, and a recall of 63%, showing that Naïve Bayes performs effectively on limited data, though it is less sensitive to minority classes. The analysis revealed that most parents expressed positive perceptions, indicating growing trust that SMPN 40 Samarinda can support students’ character development. These findings emphasize the importance of strengthening school image and service quality while highlighting the potential of machine learning–based sentiment analysis as a data-driven approach to understanding educational perceptions.
Implementasi Bot Whatsapp untuk Layanan Informasi Frontline: Studi Kasus: STMIK Widya Cipta Dharma Putra, Muhammad Sadam Saktia; Azahari; Pratiwi, Heny
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 2 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

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

Abstract

This study implements a WhatsApp bot as an automated information service at STMIK Widya Cipta Dharma to assist frontline staff tasks. The system was developed using Node.js and the WhatsApp Web API (Baileys Library) with a rule-based matching approach that maps user keywords to predefined responses. The Waterfall method was applied through analysis, design, implementation, and Black Box Testing. The results show that the bot correctly answered 85% of queries and improved information service efficiency by up to 70%.
NAÏVE BAYES-BASED STUDENT ACHIEVEMENT PREDICTION SYSTEM Angreani, Fadillah; Pratiwi, Heny; Saad, Muhammad Ibnu
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 1 (2025): Desember 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i1.4238

Abstract

Abstract: SMP Muhammadiyah 5 Samarinda still relies on manual evaluation with limited data analysis tools in predicting student academic achievement. This study aims develop a system for predicting the learning achievement of students at SMP Muhammadiyah 5 Samarinda using the Naive Bayes classification method. The dataset used consists of 192 student exam scores covering academic scores, attendance, parents’ education and income, and living conditions as independent variables, while the dependent variable is the achievement label (achieved or not achieved). The preprocessing stage includes label normalization, feature selection, and median imputation to handle missing data. The dataset was divided into 75% training data and 25%. The model was implemented as a pipeline consisting of a median imputer and a Gaussian Naive Bayes classifier. The evaluation results showed that the model achieved an accuracy of 79.2%, with a perfect recall value (1.00) in the high-achieving class and (0.64) in the low-achieving class. This shows that the model is quite effective in identifying high-achieving students. The trained model was then integrated into a Flask-based web application, which enables online predictions through a simple form interface, facilitating contextual interpretation. This system is expected to assist in educational decision-making by helping teachers identify students’ achievement levels early on and design more targeted learning interventions. Keywords: academic performance; educational data mining; naive bayes; prediction system; student achievement Abstrak: SMP Muhammadiyah 5 Samarinda masih bergantung pada evaluasi manual dengan alat analisis data terbatas dalam melakukan prediksi prestasi akademik siswa. Penelitian ini bertujuan mengembangkan sistem prediksi prestasi belajar siswa SMP Muhammadiyah 5 Samarinda menggunakan metode klasifikasi Naive Bayes. Dataset yang digunakan terdiri atas 192 data nilai ujian siswa yang mencakup skor akademik, kehadiran, pendidikan dan pendapatan orang tua, serta kondisi tempat tinggal sebagai variabel independen, sedangkan variabel dependen berupa label prestasi (berprestasi atau tidak berprestasi). Tahap preprocessing meliputi normalisasi label, seleksi fitur, serta imputasi median untuk menangani data yang hilang. Dataset dibagi menjadi 75% data latih dan 25%. Model diimplementasikan dalam bentuk pipeline yang terdiri atas median imputer dan Gaussian Naive Bayes classifier. Hasil evaluasi menunjukkan bahwa model mencapai akurasi sebesar 79,2%, dengan nilai recall sempurna (1,00) pada kelas berprestasi dan lebih rendah (0,64) pada kelas tidak berprestasi. Hal ini menunjukkan bahwa model cukup efektif dalam mengidentifikasi siswa berprestasi. Model yang telah dilatih kemudian diintegrasikan ke dalam aplikasi web berbasis Flask, yang memungkinkan prediksi secara daring melalui antarmuka formulir sederhana untuk mendukung interpretasi kontekstual. Sistem ini diharapkan dapat membantu untuk pengambilan keputusan dalam pendidikan dengan membantu guru mengidentifikasi tingkat prestasi siswa sejak dini dan merancang intervensi pembelajaran yang lebih terarah. Kata kunci: prestasi akademik; penambangan data Pendidikan; naive bayes; sistem prediksi; prestasi siswa
Design of a Web-Based Management Application for Jamu Bu Tri Shop with Sales Analysis Features Daru Caraka; Heny Pratiwi; Vilianty Rafida
Poltanesa Vol 26 No 2 (2025): December 2025
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v26i2.3555

Abstract

Digital transformation that comes from information technology has been the main game changer for businesses of all sectors. Even the least expected ones, like micro, small and medium enterprises that are MSMEs like herbal medicine shops in Indonesia, have been impacted. Jamu Bu Tri Shop is using a manual management system which leads to problems such as inaccurate data recording, lack of real-time product tracking, slow transaction processes, and difficult financial reporting. This study is about a web-based store management application with sales analysis features through the use of the Rapid Application Development methodology. Observation, interview of the owner and employees of the shop and documentation were the data collection methods. The system design was done through the Unified Modeling Language diagrams like Use Case Diagrams, Activity Diagrams, and Class Diagrams. This application can automate product management, sales transactions, category and supplier management, expense recording, and integrated report generation. The main novelty is an analytical dashboard that provides the interaction of data visualization through the line chart, bar graph, and donut chart. Black Box testing checked all system functions with 100% accuracy in eight main modules and the System Usability Scale evaluation gave a score of 91.67 with Grade A. The implementation results showed that there were considerable improvements in the operation: transaction recording time was reduced by 90%, monthly report preparation was enhanced by 98%, product stock checking was improved by 94%, and best-selling product identification was sped up by 99.7%.
Comparison Analysis of K-Nearest Neighbor and Naïve Bayes Methods in Classifying Academic Reference Books Chandra Panca Wibawa; Heny Pratiwi; Andi Yusika Rangan
Poltanesa Vol 26 No 2 (2025): December 2025
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v26i2.3556

Abstract

This study compares the performance of the K-Nearest Neighbor (KNN) and Multinomial Naïve Bayes (MNB) algorithms in classifying academic reference books based on their titles within the STMIK Widya Cipta Dharma library system. A dataset consisting of 2,153 cleaned book records was processed using the Knowledge Discovery in Databases (KDD) framework, including data selection, preprocessing, transformation, and classification. Book titles were normalized and transformed into numerical features using TF-IDF with unigram and bigram extraction. The dataset was split using a 75%–25% ratio, resulting in 1,614 training samples and 539 testing samples. Experimental results show that the KNN classifier achieves an accuracy of 72.72%, outperforming Multinomial Naïve Bayes with an accuracy of 62.70%. Confusion matrix analysis shows that KNN correctly classifies more book titles across categories. The superior performance of KNN is attributed to the sparse and short-text nature of book titles, which benefits distance-based similarity. These findings highlight the potential of machine-learning-based automated classification to improve cataloging and information retrieval in academic libraries.
Co-Authors Abed Nego Achmad Sadzali Muftisjar Ade Maulana Anshari Adeputra, James Ahmad Abul Khair Ahmad Fahrijal Pukeng Ahmad Fahrijal Pukeng Ahmad Fahrijal Pukeng Ahmad Fajri Ahmad Rofiq Hakim Ahmad Sabirin Aisyah Fajrianti Aisyah Fajriantini Akhmad Rizky Fahrozy Aldianur Fajri Alysa Anggelia Y Amelia Yusnita Ananta Putra, Resifa Andi Yusika Rangan Anggra Prima Angreani, Fadillah Anwar, Rafidan Arsita Ashari Ramadani Atventitus Etwin Loho Azahari Azahari Azahari Azahari Azahari Azahari Bai' Fathur Rayhan Bartolomius Harpad Cembes, Yosefina Chandra Panca Wibawa Cintami Amanda Putri Damaya, Filio Angga Dana Aulia Rahman Daru Caraka Daud Yefkanius Nassa Daud, Jundro Dendy Kurniawan Dessy Purnamasari Dovist Calvino Ekawati, Hanifah Ekawati, Hanifah Eko Junirianto Fadjri Astra Ryan Sinurat Harianto, Kusno Haristyawan, Ivan I Made Borneo Setyawan Ita Arfyanti Julio Enrico Frans Frans Kristian Vandi Hermawan Kristianus Catur Prasetya Ajang Kusno Harianto Kusno Harianto Lamsi, Rahmadiansyah Zain M. Irwan Ukkas Irwan Ukkas Ukkas M.Ariya Parengrengi Muhammad Alamsyah Zakaria Muhammad Andrian Muhammad Fachri Sanjaya Muhammad Fadhilah Muhammad Fahmi Muhammad Fahmi Muhammad Fahriawan Muhammad Ibnu Sa'ad Muhammad Ibnu Sa'ad Muhammad Ibnu Saad Saad Muhammad Ibnu Sa’ad Muhammad Raihan Ramandha Putra Muhammad Rega Praduana Muhammad Sadam Saktia Putra Novandra Satria Winata NUR FITRIANI Nursobah, Nursobah Nurul Hikmah Okvi Marsi Angela Claudia Pahrudin, Pajar Pitrasacha Adytia Putra, Muhammad Sadam Saktia Putri Wulandari Renni Mayasari Resifa Ananta Putra Rifka Karin Afinda Rizky Zakaryya Rasyad Ryan Artanto Halim SA'AD, MUHAMMAD IBNU Saad, Muhammad Ibnu Salmon Salmon Salmon Sarifmata Purnomo Sa’ad, Muhammad Ibnu Shinta Palupi Suhariyadi, Yonatan Sururi, M Za’iem Susi Salviati Syamsuddin Mallala Syamsuddin Mallala Ulfa Nurfadhila W Wahyuni, W Wahyuni - Wahyuni Y Yunita Yunita Yunita Zakaria, Muhammad Alamsyah