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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research JURNAL MEDIA INFORMATIKA BUDIDARMA PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer JMM (Jurnal Masyarakat Mandiri) Sebatik JURNAL PENDIDIKAN TAMBUSAI Jurnal Ilmiah Media Sisfo Journal of Information Technology and Computer Engineering JURTEKSI Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal JOURNAL OF SCIENCE AND SOCIAL RESEARCH EXPLORE Jurnal Review Pendidikan dan Pengajaran (JRPP) Jurnal Teknologi Informasi dan Pendidikan Jusikom: Jurnal Sistem Informasi Ilmu Komputer bit-Tech Jurnal Sistem Informasi dan Informatika (SIMIKA) JATI (Jurnal Mahasiswa Teknik Informatika) Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Infortech Jurnal Pendidikan Guru (JPG) Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Majalah Ilmiah UPI YPTK Journal of Computer Scine and Information Technology Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Ipteks Terapan : research of applied science and education Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence EXPLORE Jurnal Komtekinfo Journal of Computers and Digital Business SmartComp JOURNAL OF COMMUNITY SERVICE AND APPLICATION SCIENCE (JCSAS) Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Jurnal Pustaka Robot Sister
Sovia, Rini
Universitas Putra Indonesia YPTK Padang, Padang
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Implementasi Metode Profile Matching dalam Sistem Pendukung Keputusan untuk Seleksi Penerimaan Siswa Baru Mhd Wedo; Gunadi Widi Nurcahyo; Rini Sovia
bit-Tech Vol. 7 No. 3 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i3.2229

Abstract

Kemajuan teknologi informasi telah memberikan kontribusi signifikan dalam berbagai bidang, termasuk pendidikan. Salah satu tantangan dalam dunia pendidikan adalah proses seleksi penerimaan siswa baru yang sering kali memerlukan pengambilan keputusan yang cepat, objektif, dan akurat. Penelitian ini bertujuan untuk mengembangkan Sistem Pendukung Keputusan (SPK) berbasis web dengan menerapkan metode Profile Matching dalam proses penerimaan siswa baru di SMPN 1 Kerinci. Metode Profile Matching dipilih karena kemampuannya dalam membandingkan kompetensi individu dengan standar yang telah ditetapkan, sehingga dapat mengurangi subjektivitas dalam proses seleksi. Penelitian ini menggunakan pendekatan kuantitatif dengan metode eksperimen, yang melibatkan pengumpulan data nilai akademik dan non-akademik calon siswa, serta implementasi algoritma Profile Matching dalam sistem berbasis web. Hasil penelitian menunjukkan bahwa sistem yang dikembangkan mampu meningkatkan efisiensi dan akurasi proses seleksi dengan mengurangi waktu yang dibutuhkan dalam penilaian serta memberikan hasil yang lebih transparan. Pengujian sistem dilakukan menggunakan metode black box testing, yang menunjukkan bahwa semua fitur sistem berfungsi dengan baik. Selain itu, analisis perbandingan dengan metode seleksi konvensional menunjukkan peningkatan objektivitas dalam pengambilan keputusan. Dengan demikian, penerapan SPK berbasis web dengan metode Profile Matching dapat menjadi solusi inovatif bagi institusi pendidikan dalam meningkatkan transparansi, akurasi, dan efisiensi seleksi penerimaan siswa baru. Penelitian ini diharapkan dapat menjadi referensi dalam pengembangan sistem serupa di berbagai lembaga pendidikan lainnya.
Analisis Prediksi Penjualan Suku Cadang Motor dengan Metode Monte Carlo Edo Rinaldi Rais; Rini Sovia; Sumijan
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.v8i1.2231

Abstract

Peramalan penjualan merupakan salah satu aspek penting dalam strategi manajemen bisnis, terutama dalam industri otomotif yang memiliki pola permintaan yang fluktuatif. Manajemen stok yang tidak optimal dapat menyebabkan overstock atau stockout, yang berdampak pada efisiensi operasional dan kepuasan pelanggan. Penelitian ini bertujuan untuk menerapkan metode Monte Carlo dalam memprediksi penjualan suku cadang motor di Bengkel Ilham Motor, guna meningkatkan akurasi prediksi dan membantu optimalisasi pengelolaan persediaan barang. Metode penelitian ini menggunakan data historis penjualan tahun 2024, yang dianalisis melalui beberapa tahapan: penentuan distribusi probabilitas, pembangkitan angka acak, simulasi Monte Carlo, dan validasi hasil prediksi. Implementasi metode ini dikembangkan dalam sistem berbasis web, menggunakan PHP sebagai bahasa pemrograman dan MySQL sebagai basis data. Hasil penelitian menunjukkan bahwa metode Monte Carlo mampu memberikan tingkat akurasi prediksi yang tinggi, dengan rincian sebagai berikut: oli (95,33%), kampas rem (99,59%), lampu depan (97,27%), saringan udara (97,53%), busi (95,78%), dan sil karet (97,32%). Prediksi yang dihasilkan memungkinkan bengkel untuk menentukan jumlah stok yang lebih optimal, sehingga dapat menghindari kelebihan maupun kekurangan persediaan. Selain itu, sistem berbasis web yang dikembangkan terbukti dapat mempercepat analisis data dan membantu dalam pengambilan keputusan bisnis yang lebih akurat. Kesimpulan dari penelitian ini adalah bahwa metode Monte Carlo dapat diandalkan sebagai pendekatan prediktif dalam perencanaan stok suku cadang motor. Untuk pengembangan lebih lanjut, disarankan agar model ini dikombinasikan dengan teknik machine learning atau mempertimbangkan faktor eksternal seperti tren pasar dan harga bahan baku guna meningkatkan akurasi prediksi.
Analysis of Clean Water Consumption Segmentation And Classification Using K-Means Clustering And Random Forest Algorithms Ika Melinia Sapitri Fitriyanti; Sarjo Defit; Rini Sovia
Jurnal KomtekInfo Vol. 13 No. 1 (2026): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

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

Abstract

The administrative grouping of PERUMDA Air Minum Kota Padang customers is not yet able to accurately represent actual customer water consumption patterns. This condition makes it difficult for the company to formulate service policies, customer management, and make appropriate data-based decisions. This study aims to analyze and map customer water consumption patterns to produce more representative customer segmentation as a basis for decision making. The research method used is a data mining approach with the application of Principal Component Analysis (PCA) for dimension reduction, K-Means Clustering for customer segmentation, and Random Forest for customer classification, using primary data from the Padang City Water Company's Customer Meter Reading Report with an initial amount of 371 data. The results of the study show that the clustering process successfully formed three customer segments, namely premium customers with high consumption bills, regular customers with moderate and stable consumption, and new customers with low consumption rates. The evaluation of the Random Forest model's performance resulted in an accuracy rate of 68.85% on the training data and 67.69% on the testing data, with an average precision value above 0.84 and an average F1-score value of around 0.68. The consistency of performance between the training data and the testing data shows that the model has fairly good generalization capabilities and does not experience overfitting.
Language Processing for Detecting Fake News on Twitter Using a Long Short-Term Memory Architecture Rini Sovia; Dwi Andhara Valkyrie; Ruri Hartika Zain; Firdaus
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6570

Abstract

The rapid spread of misinformation on social media platforms, particularly X (formerly Twitter), poses a significant challenge to public trust and democratic integrity. Fake news is often crafted to deceive readers and manipulate public opinion, especially in political contexts such as the 2024 Regional Head Elections (Pilkada 2024). Although various measures have been proposed to mitigate this issue, achieving an effective balance between controlling misinformation and preserving free speech remains a challenge. This study aims to address this problem by developing a fake news detection model based on Natural Language Processing (NLP) and Long Short-Term Memory (LSTM). The dataset used in this study was collected from public tweets related to Pilkada, with Kompas.com serving as the validation source to verify content authenticity. Experimental results show that the proposed LSTM model outperformed traditional classification methods, achieving a precision, recall, and F1-score of 0.95, along with an overall accuracy of 94.90%. Confusion matrix analysis further confirmed the reliability of the model by demonstrating low misclassification rates. This study contributes to the advancement of AI-driven hoax detection systems, offering an automated and scalable solution for combating misinformation in political discourse.
Determining Alternative Mechanical Quality of Aluminum for Making Ordered Equipment Using the Multifactor Evaluation Process (MFEP) Method Lony Armawati Tambunan; Rini Sovia; Wifra Safitri
Journal of Computer Scine and Information Technology Volume 9 Issue 4 (2023): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v9i4.86

Abstract

Many various types of industrial companies use aluminum to support their industrial productivity. One of the reasons companies use this aluminum material is because aluminum is a good electrical conductor, light and strong. In determining which aluminum metal is suitable to be used to make ordering equipment, of course the metal with the best quality must be chosen so that the production of aluminum equipment is in demand by many consumers. Multi Factor Evaluation Process (MFEP) method, all criteria which are important factors in making considerations are given weighting. (weighting) is appropriate. Decision making using the Multi Factor Evaluation Process method is carried out subjectively by considering several factors that influence alternatives. While selecting metal, SMEs still record manually, so it takes quite a long time to make decisions. Not only that, determining the quality of aluminum metal also involves visual observations just by looking at the durability and assessing the physical metal. To overcome the problems faced by the old system. then a new system was formed, where the selection process could be carried out by Toko Berkah Qory Siregar Aluminum without waiting a long time . The results obtained based on calculations are Aluminum A, namely Blue Sky Aluminum with a preference value of 100.
Perancangan Sistem Peminjaman Papan Surfing Menggunakan Rfid, Barcode Scanner Dan Delphi 7 Riska Amelia; Rini Sovia; Ruri Hartika Zain
Journal of Computer Scine and Information Technology Volume 10 Issue 1 (2024): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

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

Abstract

Water surfing has become a tourism industry worth billions, where millions of surfers travel around the world to several water surfing destinations in search of the 'perfect wave'. It is now estimated that there are more than 10 million water surfers in the world and this continues to increase at 12-16% per year. These surfers also visit places in the world that they feel are in harmony with what they are looking for. In one place they visited there were surfboard rental kiosks.Surfboard rental service sellers will serve visitors to rent their surfboards. Because there are too many surfboard enthusiasts, it is too difficult for surfboard rental service sellers to serve visitors. From these problems, a system is needed that can simplify the process and data collection of surfboard borrowing.
Implementasi Metode Yolov10 Untuk Mendeteksi Penyakit Melalui Analisis Citra Daun Pada Tanaman Padi Encik Yoega Renaldi; Sumijan Sumijan; Rini Sovia
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 14, No 4 (2025): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v14i4.8486

Abstract

Padi menjadi makanan pokok bagi hampir 80% untuk diseluruh Indonesia, yang penghidupannya sangat bergantung pada hasil panen. Sektor pertanian padi menghadapi tantangan berupa penyakit pada daun tanaman, dengan mayoritas petani masih menggunakan metode konvensional dalam deteksi penyakit, menyebabkan keterlambatan penanganan. Penelitian ini mengembangkan sistem deteksi dini penyakit tanaman padi menggunakan kecerdasan buatan dan computer vision dengan deep learning. Implementasi metode YOLOv10 yang efektif dengan menghilangkan penekanan Non-Maximum Suppression untuk mengurangi komputasi secara signifikan. Data penelitian yang dikumpulkan di Dinas Pertanian Kota Padang mencakup 1.446 citra dari tiga jenis penyakit: hawar daun bakteri, cendawan bercak, dan virus tungro. Pre-processing melalui augmentasi data, dataset diperbesar menjadi 10.122 citra. Pelatihan model selama 100 epoch menghasilkan tingkat kepercayaan untuk penyakit daun bakteri hawar (90%), cendawan bercak (91%), dan virus tungro (98%). Sistem mencapai tingkat kepercayaan mAP 93%, Skor F1 88%, dengan waktu komputasi 0,9 detik per citra. Sistem ini menjadi solusi efektif dan efisien bagi para ahli pertanian dan petani dalam menganalisis tingkat keparahan penyakit daun pada tanaman padi.
An Analysis of Public Satisfaction with Government Services: A Multi-Method Approach Using PCA, K-Means Clustering, and Linear Regression Abuzar Gafari; Sarjon Defit; Rini Sovia
Sebatik Vol. 30 No. 1 (2026): June 2026
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v30i1.2742

Abstract

Flawless performance evaluation results across all service dimensions may potentially obscure the identification of areas for improvement and diminish objectivity in decision-making. This study aims to identify the specific service attributes influencing public satisfaction and to segment respondents based on their satisfaction levels at the Office of the Ministry of Religious Affairs in Payakumbuh City. The research integrates Principal Component Analysis (PCA), K-means clustering, and linear regression. PCA was employed to reduce data dimensionality and establish principal components; K-means clustering was utilized to group respondents based on perceptual similarities regarding service quality; and linear regression was applied to identify the most significant factors influencing public satisfaction within each segment. The data were sourced from the Public Service Survey Information System (SISULAP) application of the Payakumbuh Ministry of Religious Affairs, spanning June 2024 to October 2025, with a total of 1,950 respondents. The findings reveal that service process and efficiency are the primary factors influencing all respondent segments, with the low-satisfaction segment identified as the top priority for service improvement. The regression models demonstrate robust performance across all segments. These findings provide an empirical foundation for data-driven policymaking to enhance public service quality.
Sentiment Analysis of Public Comments on YouTube Content Using Principal Component Analysis and Naive Bayes Dede Pratama; Sumijan Sumijan; Rini Sovia
Sebatik Vol. 30 No. 1 (2026): June 2026
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v30i1.2746

Abstract

The rapid acceleration of digital media development compels public broadcasting institutions to adapt to shifting public information consumption patterns, which are now centered on online platforms. TVRI Sumatera Barat has responded to these dynamics by leveraging YouTube as a channel for content distribution and audience engagement. However, this interaction generates a massive volume of unstructured comment text, rendering manual sentiment analysis inefficient, time-consuming, and prone to subjectivity. This study aims to address these challenges by automatically and objectively classifying user sentiment using a machine learning approach. The applied methodology integrates Principal Component Analysis (PCA) and the Gaussian Naive Bayes algorithm. PCA serves as a dimensionality reduction technique to simplify TF-IDF weighted text features without losing vital information, while Gaussian Naive Bayes was selected for classification due to its efficiency in rapidly processing the continuous numerical data resulting from the PCA transformation. The research dataset comprises 10 comments from the TVRI Sumatera Barat YouTube channel in 2024, collected via the YouTube Data API, which underwent preprocessing and labeling for positive and negative sentiments. Model validation was conducted using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The test results demonstrate that the combination of PCA and Gaussian Naive Bayes effectively enhances computational efficiency and delivers precise classification performance. This research makes a significant contribution by providing a measurable method for public opinion analysis, which is essential as a basis for evaluating audience perception to improve the quality of digital broadcasting strategies in public institutions.
Optimalisasi Strategi Pembelajaran Siswa Melalui Identifikasi Gaya Belajar Menggunakan Klasterisasi K-Means dan Klasifikasi K Nearest Neighbor Ilsa Hidayat; Musli Yanto; Rini Sovia
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i3.9322

Abstract

Accuracy in adjusting teaching strategies to student learning characteristics is important because it can determine the effectiveness of the learning process. One of the key factors in improving the quality of learning is the suitability between teachers' teaching strategies and students' learning styles. The mismatch between the two aspects can reduce the effectiveness of the learning process and have an impact on low learning outcomes. Based on this, this study aims to optimize students' learning strategies through the application of the K-Means clustering model and the K-Nearest Neighbor classification. The performance of the K-Means Algorithm is able to classify learning styles and determine the labeling of learning styles, K-Nearest Neighbor is used to classify data that has been labeled by the K-Means algorithm. This research dataset amounted to 200 student data sourced from SMP Negeri 1 Panyabungan from the results of 20 questions answered by students. The results showed that the combination of the K-Means and K-Nearest Neighbor algorithms produced good performance with an accuracy value of 0.92, precision of 0.92, recall of 0.92, and F1-score of 0.91. The contribution of this research is expected to enrich the literature related to the application of the K-Means and K-Nearest Neighbor models in optimizing learning strategies, as well as assisting teachers at SMP Negeri 1 Panyabungan in designing and implementing learning strategies that are more effective and in accordance with the needs of students.
Co-Authors Abuzar Gafari Adiddo Restiady Adinda Syalsabila Aditra Agung Ramadhanu Agus Salim, David Ahsan Firdaus Ali Nurdiansyah Amin Amirul Mukminin, Andi Anam, M Khairul Anggy Wahyudi Aulia Fitrul Hadi Aulia Fitrul Hadi Ayu Mahessya, Raja Billy Hendrik Borianto, B Chairunnissa Deliva Akbar, Syifa Dede Pratama Deny Suyandi Deval Gusrion Devi Maryuni Devia Kartika Dila, Rahmah Dwi Andhara Valkyrie Dwiki Aulia Fakhri Edo Rinaldi Rais Effendy, Geraldo Revanska Eka Praja Wiyata Mandala Elmi Rahmawati Elmi Rahmawati, Elmi Encik Yoega Renaldi Erlanda, Hadrian Fana, Wulan Stau Fatimah, Noor Firdaus daus Firna Yenila Gema, Rima Liana Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo Guslendra Gusriva, Revi Hadi, Aulia Fitrul Hadiyanto, Tegas Hadrian Erlanda Hanippa Prima Putra Harnaranda, Jefri Hartika Zain, Ruri Hartika Hasri Awal Hendri Irawan Hendri Irawan Heriyanto Hoka Muhgrah Sandawa Huda, Ramzil Ika Melinia Sapitri Fitriyanti Ilsa Hidayat Irzal Arif Wisky Islam, Md Ataul Jimmy Febio Julsapargi Nursam Kharisma Utama Putra Khomsi, Ahmad Lidya Adriani Darma Lony Armawati Tambunan Lubis, Fitri Amelia Sari Lusinia, Shary Armonitha Maha Rani maha rani Mardhiah, Sitty Mhd Wedo Muhammad Aidil Rahman Muhammad Ikhsan Al-Arrafi Muhammad Reza Putra Muhammad, Abulwafa Mutiana Pratiwi Nabilla Yasmin Niken Rindiana Nugraha, Fajri Nurdiansyah, Ali Nursam, Julsapargi Nursyahrina Oriza Rama Saputra Permana, Randi Permana, Randy Prihandoko Puja M Alca Putra, Kharisma Utama Putri Melati Putri Melati Rahma Yanti Rahmad Rahmad Rahmad Rahmad Rahman, Muhammad Aidil Rahman, Zumardi Rahmi, Nadya Alinda Ramadani, Sela Randa Mahardika Randy Permana Randy Permana Retno Devita Revi Gusriva Ricki Ardiansyah ricki ardiansyah Ricki Ardiansyah Ricki Ardiansyah, Ricki Ridwan Sutri Ridwan Sutri Rinaldi Chan, Fajri Rindhani Aditia, Mellya Riska Amelia Riyan Saputra Riyan Saputra, Riyan Roza, Yesi Betriana Rozakh, Muhammad Ruri Hartika Zain Ruri Hartika Zain Ruri Hartika Zain S, Sumijan Sandawa, Hoka Muhgrah Saputra, Charisman Fajri Saputra, Oriza Rama Saputra, Randy Sarjon Defit Sarjon Defit Selfi Melisa Selvia, Dina Shally Amna Silky Safira Siregar, Diffri Sulastri Sulastri Sumijan Sumijan Syafri Arlis Syafril Syafril Syafril Syafril Syafril Syafril Syaiffullah, Afif Tika Christy Tsalsabila Jilhan Haura Tsalsabila Jilhan Haura Tuti Nabila Wahyudi, Anggy Widya Nursanty Wifra Safitri Wirdawati, Wira Yanti, Rahma Yanto, Musli Yanto, Musli Yasmin, Nabilla Yesi Betriana Roza Yuhandri Yuhandri, Yuhandri Zainal A. Haris