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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Teknoinfo MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Pengabdian Magister Pendidikan IPA Jurnal Teknologi Informasi dan Multimedia Jurnal Informatika dan Rekayasa Elektronik Jurnal Bumigora Information Technology (BITe) ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesian Journal of Networking and Security - IJNS Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Journal of Artificial Intelligence and Digital Business Bakti Sekawan : Jurnal Pengabdian Masyarakat Jurnal Ilmiah Pengabdian dan Inovasi Nanggroe: Journal Of Scholarly Service Jurnal Riset Teknologi dan Inovasi Pedidikan (JARTIKA) Nanggroe: Journal of Scholarly Service Journal Of Artificial Intelligence And Software Engineering Journal of Information Systems Management and Digital Business ORAHUA : Jurnal Pengabdian Kepada Masyarakat JATISKOM: Jurnal Aplikasi Teknologi Informasi dan Sains Komputer Jurnal Pengabdian Pada Masyarakat IPTEKS
Ria Rismayati
Universitas Bumigora
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management 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 Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Other

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Sentiment Analysis of Service and Facility Satisfaction at Computer Lab of Universitas Bumigora Using Indobert Mundika, Eko; Martono, Galih Hendro; Rismayati, Ria
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6798

Abstract

Computer laboratories have a strategic role in supporting the technology-based learning process at Bumigora University. To understand the extent to which the available services and facilities meet students' expectations, this study conducted a sentiment analysis of student reviews using the IndoBERT model, an artificial intelligence-based Natural Language Processing (NLP) approach. Data was obtained from a questionnaire focusing on aspects of laboratory services and facilities, then analyzed to classify opinions into positive, negative, and neutral sentiments. The analysis results show the dominance of positive sentiments, indicating that computer laboratories have generally met student expectations, especially in supporting practicum activities. The IndoBERT model used was able to achieve 85% accuracy, demonstrating its effectiveness in reliably identifying opinion trends. These findings provide a comprehensive picture of student perceptions, and serve as an important basis for managers in formulating strategies to improve the quality of laboratory services and facilities so that a conducive and relevant learning experience can be maintained.
Pendampingan Kemandirian Berwirausaha Produk Frozenfood Melalui Pemberdayaan Anak-Anak Panti Asuhan Al Hidayah Tanjung Karang Rahima, Phyta; Ria Rismayati
Jurnal Pengabdian Magister Pendidikan IPA Vol 8 No 1 (2025): Januari-Maret 2025
Publisher : Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jpmpi.v8i1.10800

Abstract

Kegiatan Pengabdian yang berjudul Pendampingan Kemandirian Berwirausaha Produk Frozenfood Melalui Pemberdayaan Anak-Anak Panti Asuhan Al Hidayah Tanjung Karang kami lakukan pada tanggal 5 Desember 2024. Tujuan dari Kegiatan ini adalah Membentuk Jiwa mandiri untuk berwirauasaha terutama bagi anak anak Panti Asuhan Al hidayah. Metode yang dilakukan untuk kegiatan ini adalah Penyuluhan dan cara membuat makanan olahan frozenfood yang layak jual. Kegiatan ini melibatkan sebanyak kurang lebih 50 Orang anak anak Panti Asuhan yang dimulai dari jam 8 Pagi sampai dengan jam 5 Sore WITA. Semua alat dan Bahan disipakan oleh kami selaku Tim Pengabdian sehingga kegiatan ini dapat berjalan dengan baik. Dan hasil yang diperoleh adalah anak anak panti asuhan dapat dengan baik mengikuti arahan dari Tim dan mudah mencerna semua kegiatan yang diberikan.
Penguatan Kemampuan Teknis Desain Grafis Percetakan bagi Siswa SMK di Samudane, Kabupaten Lombok Tengah Pribadi, Agus; Yunus, Muhammad; Rismayati, Ria
Jurnal Pengabdian Pada Masyarakat IPTEKS Vol. 2 No. 2: Jurnal Pengabdian Pada Masyarakat IPTEKS, Juni 2025
Publisher : CV. Global Cendekia Inti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71094/jppmi.v2i2.107

Abstract

Nowadays, most of SMK’s graduates in Central Lombok Regency are not yet ready to directly apply their skills and potential to work place. Multimedia expertise field of Graphic Design competency for Printing products is a favorite choice for prospective SMK’s students in Central Lombok Regency. Less than optimal learning is often experienced by SMK in remote areas, especially non-state schools; mostly due to limited learning devices. SMK in remote areas and private schools accommodate many students who cannot attend state schools which are generally located in the center city or other urban area. The field conditions that occur are the lack of graduate capacity to meet the needs of graduate users. Main problem faced by SMK is the lack of device support in learning and learning experience in practical derived from the world place. Implementation of the Community Service Program provides an answer to these needs. SMK Al Fajri as a partner becomes a accommodator for workshop activities to assist in increasing the capacity of students in preparing Graphic Design for Printing products. Students are given practical training and work on design projects as training to strengthen their technical skills. Communication and interaction exercises are added to complement students' competencies. Based on the evaluation, all workshop participants gained new experiences, increased technical and non-technical capacities. 80% of students can create Graphic Designs for Printing products independently and based on orders, without having to be given examples and direct guidance.
Comparison of Random Forest, Decision Tree, and XGBoost Models in Predicting Student Academic Success Nurbaeti, Nurbaeti; Sulistiyaningsih, Neny; Rismayati, Ria
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7138

Abstract

Students' academic success is influenced by various academic and non-academic factors. Machine learning (ML) offers an effective approach to predicting academic outcomes by analyzing complex data patterns. However, most previous studies are limited to graduation prediction and rarely incorporate non-academic features or multiple feature selection techniques. This study aims to compare the performance of three ML algorithms Random Forest, Decision Tree, and XGBoost in classifying students’ academic success using a dataset from the UCI Machine Learning Repository, consisting of 4424 records and 37 features. The data underwent cleaning, label transformation, and feature selection using PCA, SelectKBest, and Variance Threshold. Models were trained using a holdout method (80% training, 20% testing) and evaluated based on accuracy, precision, recall, and F1-score. The results show that Random Forest with Variance Threshold achieved the highest accuracy (0.77) and F1-score (0.84) on majority classes. XGBoost followed with 0.75 accuracy, while Decision Tree showed the lowest performance. All models struggled to classify the minority class, indicating challenges related to data imbalance. This research highlights the importance of algorithm choice and effective feature selection in academic classification tasks. It also emphasizes the need for data balancing strategies to reduce class bias. The findings can help educational institutions design data-driven interventions to improve learning outcomes and reduce dropout rates.
Klasifikasi Gizi Lansia Menggunakan Metode Naïve Bayes Classifier Kartarina Kartarina; Adelia Azzahrah Hatina; Ria Rismayati; Baiq Fitria Rahmiati; Fatimatuzzahra Fatimatuzzahra; Rahayun Amrullah Husaini
Jurnal Teknologi Informasi dan Multimedia Vol. 6 No. 2 (2024): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v6i2.502

Abstract

Elderly people are a group that is vulnerable to experiencing various problems in terms of nutrition and health caused by changes in eating patterns. Nutritional status affects the independence of an elderly person, where good nutritional status means less dependence on other people and vice versa. It is necessary to treat malnutrition or malnutrition as early as possible, one of which is by having an elderly posyandu. Posyandu for the elderly as a community service provides services and assistance in special health for the elderly, by regularly recording, controlling and reviewing the medical records of the elderly in a document. The data processing method in this research uses the Naïve Bayes method, where the data used comes from the medical records of the elderly and then used as a reference as to whether the elderly have good nutrition or are malnourished and require further action. Medical record documents play an important role in posyandu services for the elderly, so that medical record documents should be digitally based and systematic in recommending the nutritional status of the elderly. The Naïve Bayes algorithm is an algorithm that can help in classifying data in diagnosis using criteria for the condition of elderly patients. Naïve Bayes also has precise accuracy when implemented in applications that have databases with large data and makes it easier for users to interpret the results. This is proven by this research which produces an accuracy value of 91% with the data used as a sample of 110 elderly patients. The system design aims to help users as posyandu cadres in knowing whether the condition of the elderly is good, whether the elderly are at risk of malnutrition and provide treatment that is appropriate to the condition of elderly patients as well as assisting the Posbindu PTM in transforming documents into computerized ones.
Architecture Enterprise Program Studi S1 Teknik Informatika dengan TOGAF Architecture Development Method (Studi Kasus : STMIK Bumigora Mataram) Ni Gusti Ayu Dasriani; Ria Rismayati
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 18 No. 1 (2018)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v18i1.342

Abstract

Program Studi S1 Teknik Informatika yang bernaung dibawah Sekolah Tinggi Manajemen Informatika dan Komputer (STMIK Bumigora Mataram), sebagai program studi dengan jumlah peminatnya mendominasi keluarga besar STMIK Bumigora Mataram. Seiring dengan bertambahnya mahasiswa disertai dengan padatnya operasional yang dijalankan oleh civitas akademik sehingga mendorong pihak internal untuk mampu mendukung fungsi bisnis dengan menyelaraskan strategi bisnis dan teknologi yang digunakan. Pencapaian sebuah keselarasan teknologi informasi dengan bisnis yang dijalankan oleh STMIK Bumigora Mataram, sehingga dirancanglang sebuah arsitektur enterprise yang mampu menghasilkan sebuah Blue Print yang dilengkapi dengan sebuah Framework TOGAF sehingga mampu menganalisis arsitektur bisnis secara lengkap dan menyeluruh untuk periode waktu jangka panjang.
Comparative Analysis of Naive Bayes and Fuzzy Logic Algorithm in Fire Classification System Ria Rismayati; Suriyati Suriyati; Parama Diptya Widayaka
SISTEMASI Vol 15, No 3 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i3.5985

Abstract

Building fires can cause losses in several areas, including property damage, environmental pollution, loss of life, injury, and psychological trauma. Building fires can occur due to several factors such as gas leaks, short circuits, overheating electronic devices, the presence of flammable materials, and human error. In fire mitigation efforts, devices are generally used as early warnings, but their implementation is often less than optimal due to system malfunctions. Therefore, this study aims to develop an early warning system that can detect potential fires before they spread. The methods used in this study are the naïve Bayes and fuzzy logic methods, which then compare each method to determine the most effective method. The results of this study indicate that the naïve Bayes and fuzzy logic methods have successfully classified potential fires well. From 30 experimental data, the naïve Bayes algorithm produced an accuracy of 96%, while the fuzzy logic algorithm produced an accuracy of 100%. The naïve Bayes algorithm shows reliable performance in classifying extreme data while the fuzzy algorithm can detect the ‘Danger’ status even though not all parameters are in a dangerous condition.
Analisis Perbandingan Metode Clustering Untuk Seleksi Penerima Bantuan Pendidikan Berbasis Data Mining Fajrin Fajrin; Apriani Apriani; Ria Rismayati
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 2 (2026): Mei-Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i2.11894

Abstract

Penentuan penerima bantuan pendidikan yang masih dilakukan secara manual rentan menimbulkan subjektivitas dan ketidaktepatan sasaran akibat keterbatasan dalam mengolah data siswa secara menyeluruh. Penelitian ini bertujuan membandingkan kinerja lima metode clustering, yaitu K-Means, Fuzzy C-Means, Gaussian Mixture Model, Spectral Clustering, dan Agglomerative Hierarchical Clustering, dalam mengelompokkan calon penerima bantuan pendidikan berdasarkan kondisi sosial ekonomi, serta menentukan metode terbaik. Penelitian menggunakan pendekatan kuantitatif deskriptif komparatif dengan dua dataset, yaitu data siswa SMKN 1 Lambu sebanyak 1.241 data sebagai objek utama dan dataset Academic Performance Retention dari Kaggle sebanyak 574 data sebagai pembanding. Atribut yang digunakan meliputi jenis tempat tinggal, alat transportasi, jumlah saudara, pekerjaan dan pendapatan orang tua, serta jarak rumah ke sekolah. Tahapan penelitian meliputi pembersihan data, transformasi, normalisasi menggunakan RobustScaler, reduksi dimensi dengan Principal Component Analysis, penerapan lima metode clustering dengan tiga cluster, serta evaluasi menggunakan Silhouette Score dan Davies-Bouldin Index. Hasil menunjukkan Agglomerative Hierarchical Clustering memberikan performa terbaik pada dataset utama dengan Silhouette Score 0,652852 dan Davies-Bouldin Index 0,597686, mengungguli K-Means, Fuzzy C-Means, Gaussian Mixture Model, dan Spectral Clustering. Metode ini menghasilkan cluster yang lebih kompak dan terpisah antarkelompok, sehingga direkomendasikan sebagai dasar sistem pendukung keputusan penentuan penerima bantuan pendidikan yang lebih objektif dan tepat sasaran.
Prediksi Tingkat Realisasi Pupuk Bersubsidi Menggunakan Support Vector Machine (SVM) Muhamad Zaril Akbar; Dadang Priyanto; Ria Rismayati
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 5 No. 2 (2026): Mei-Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v5i2.12397

Abstract

Ketidaksesuaian antara alokasi dan realisasi penyaluran pupuk bersubsidi di Provinsi Nusa Tenggara Barat (NTB) menjadi tantangan utama dalam menjaga efektivitas distribusi serta keberlanjutan produksi sektor pertanian daerah. Penelitian ini bertujuan untuk mengembangkan dan mengevaluasi model prediksi realisasi pupuk bersubsidi menggunakan algoritma Support Vector Regression (SVR) berbasis kernel Radial Basis Function (RBF). Dataset sekunder yang digunakan mencakup 1.081 data historis alokasi dan realisasi penyaluran lima jenis pupuk bersubsidi (Urea, NPK, SP-36, ZA, dan Organik) periode Januari 2017 hingga Desember 2025 di Kabupaten Lombok Barat. Tahapan metodologi meliputi pembersihan data, seleksi variabel input (tahun, bulan, RDKK, dan alokasi), serta normalisasi data menggunakan metode StandardScaler. Pembagian dataset dilakukan dengan rasio 80% data latih dan 20% data uji. Kinerja model dievaluasi menggunakan metrik Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), dan koefisien determinasi (R2). Hasil pemodelan menunjukkan bahwa algoritma SVR dengan kernel RBF mampu memodelkan hubungan nonlinier antarvariabel dengan sangat baik. Performa terbaik dicapai pada prediksi pupuk NPK dengan nilai R2sebesar 0,9607, MAE sebesar 0,13, dan RMSE sebesar 0,21, disusul SP-36 (R2 = 0,9558) dan Urea (R2 = 0,6911). Selain itu, model SVR diintegrasikan ke dalam aplikasi berbasis web yang dilengkapi fitur Early Warning System untuk membatasi rekomendasi alokasi agar tidak melampaui kuota. Penelitian ini terbukti efektif memberikan estimasi realisasi distribusi pupuk yang akurat sebagai alat pendukung keputusan bagi pengambil kebijakan pertanian daerah.
Co-Authors Adelia Azzahrah Hatina Agus Pribadi Ahmad Ahmad Ahmad Zuli Amrullah Ahmat Adil Andi Sofyan Anas Apriani Apriani Arinul Haq, M. Syaoqi Ayu Dasriani, Ni Gusti Dadang Priyanto Diah Supatmiwati Dian Darmawan Dwi Saputra Fajrin Fajrin Fatimatuzzahra Fatimatuzzahra Febri Mamluatul Ulumi Galih Hendro Martono Halimatuzzahrah Halimatuzzahrah Hasbullah Hasbullah Hilda Hastuti I Made Yadi Dharma Ismarmiaty Ismarmiaty Ismarmiaty, Ismarmiaty Kartarina, Kartarina La Dzunnafi Arridlwany Lalu Aldila Maulana Fajar Lely Kurniawati Miftahul Jannah Muhamad Zaril Akbar Muhammad Aldi Zarkashy Muhammad Innuddin MUHAMMAD TAJUDDIN Muhammad Yunus Muhammad Yunus Mundika, Eko Neny Sulistianingsih Ni Ketut Sri Winarti, Ni Ketut Sri Ni Luh Putu Merawati Ni Luh Putu Merawati Ningsih, Neny Sulistia Nur Utami Wahdaniati Nurbaeti Nurbaeti Parama Diptya Widayaka Parihin Parihin Phyta Rahima Pytha Rahima Rahayun Amrullah Husaini Rahmat Suci Rahmiati, Baiq Fitria Rifqi Hammad S, Suriyati Santi, Indyah Hartami Shofwan, Ari Sulistiyaningsih, Neny Suriyati ., Suriyati Syahrir, Moch. Syahroni Hidayat Wahyu Kamil Syarifaturrahman, Wahyu Kamil