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All Journal JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer JURNAL PENDIDIKAN TAMBUSAI Journal of Information Technology and Computer Engineering JURTEKSI Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Review Pendidikan dan Pengajaran (JRPP) Jusikom: Jurnal Sistem Informasi Ilmu Komputer bit-Tech Jurnal Sistem informasi dan informatika (SIMIKA) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Infortech Jurnal Pendidikan Guru (JPG) Journal of Computer Scine and Information Technology Bulletin of Computer Science Research Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Komtekinfo SmartComp Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Sovia, Rini
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Social Sciences Other

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Prediction of Extreme Poverty Levels Using the Performance of the Multiple Linear Regression Method Borianto, B; Yuhandri, Y; Sovia, Rini
Jurnal KomtekInfo Vol. 12 No. 3 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i3.655

Abstract

Extreme poverty is a type of poverty that is defined as a condition that cannot meet basic human needs. The Government of Indonesia through Presidential Instruction No. 4 of 2022 sets a target for the elimination of extreme poverty, but this effort requires an accurate and comprehensive data-driven approach. This study aims to build a model for predicting extreme poverty levels. The method used in this study is Multiple Linear Regression (MLR), which is able to measure the contribution of each predictor variable to the phenomenon of extreme poverty. The dataset processed in this study was sourced from the Dumai City Social and Community Empowerment Office. The dataset consisted of 2,007 extreme poverty data with predictor variables in the form of residence ownership (X1), employment (X2), income (X3), education (X4), and health insurance (X5). The results of this study show that the Multiple Linear Regression method is able to provide accurate predictions of the extreme poverty level in Dumai City with an accuracy rate of 87%. The model evaluation was carried out using three metrics based on the results of the test obtained R = 0.674 and R² = 0.454, which means that 45.4% of the variation in poverty status can be explained by the variables of home ownership, type of occupation, amount of income, education level, and health insurance. The ANOVA test showed a value of F = 332.777 with a significance of < 0.001, so the model was simultaneously significant. The regression coefficient showed that all variables had a negative and significant influence (p < 0.05) on poverty status, with the greatest influence coming from the type of job (β = -0.304) and amount of income (β = -0.291), followed by home ownership, health insurance, and education level. Thus, the Multiple Linear Regression method has proven to be effective in building an extreme poverty prediction system. This model can be a basic reference in supporting more targeted, measurable, and data-based socio-economic policy decision-making, especially in efforts to combat extreme poverty in a sustainable and systematic manner.
Implementasi Metode Yolov10 Untuk Mendeteksi Penyakit Melalui Analisis Citra Daun Pada Tanaman Padi Renaldi, Encik Yoega; Sumijan, Sumijan; Sovia, Rini
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.
Penerapan Metode Profile Matching pada Penilaian Kinerja Dosen Effendy, Geraldo Revanska; Yuhandri, Yuhandri; Sovia, Rini
Jurnal PROCESSOR Vol 20 No 2 (2025): Jurnal Processor
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/processor.2025.20.2.2502

Abstract

The evaluation of lecturer performance at Adzkia University faces challenges in terms of inefficient data processing. This research aims to implement the Profile Matching method to optimize the lecturer performance assessment system, evaluate its effectiveness, and develop an application based on this method. The research was conducted using a quantitative method employing Profile Matching, which includes several stages: GAP calculation, GAP mapping, core factor and secondary factor analysis, total value calculation, and ranking determination. The evaluation was conducted on 38 lecturers considering five main criteria: Adzkian Values, Education, Research, Community Service, and Supporting Activities, which are detailed in 28 sub-criteria. The implementation of the Profile Matching method proved to produce objective assessments by placing Lecturer 31 as the lecturer with the highest score (4.251), followed by Lecturer 3 and Lecturer 30 (4.092). The developed web-based application successfully integrated this method and improved the efficiency of the assessment process. This study demonstrates the effectiveness of the Profile Matching method in evaluating lecturer performance with more objective results. The implemented system helps BPSDM conduct assessments more efficiently and generate more structured reports.
Determination Potential Experts by Application The Apriori Algorithm and the K-Means Algorithm Sovia, Rini; Defit, Sarjon; Fatimah, Noor
International Journal of Artificial Intelligence Research Vol 6, No 1 (2022): June 2022
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (419.524 KB) | DOI: 10.29099/ijair.v6i1.219

Abstract

Experts are people who have special expertise who provide services based on their expertise. The company has experts in handling projects that will be carried out for the progress of the company. The importance of the quality of experts in the company can improve the quality of human resources. The Apriori algorithm is a data mining method that has the aim of looking for association patterns based on the project being carried out so that they can be identified by experts who are often used in handling projects. Furthermore, a data mining approach is needed to classify experts with the K-means algorithm used. This study combines the Apriori and K-means algorithms, by grouping experts based on the handling of the project they are working on.
Data Warehouse Design With ETL Method (Extract, Transform, And Load) for Company Information Centre Fana, Wulan Stau; sovia, rini; Permana, Randi; Islam, Md Ataul
International Journal of Artificial Intelligence Research Vol 5, No 2 (2021): December 2021
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (313.568 KB) | DOI: 10.29099/ijair.v5i2.215

Abstract

Data Warehouse is a technology use to analyze, extract and evaluate data into information which produce knownledge in the form of analysis to provide an advice in decision making process. Designing a Data Warehouse using ETL (Extract, Transformation and Load) process serves as the collection of data from different data sources into a multitude of integrated data sets. By using snowflake scheme for the design of the data warehouse make data prepare well and ready for analyze on Data Warehouse. The result of  this reseach is to applied Data Warehouse that use to support company decision making progress make easier and has a good decision since its come from Data Warehouse
Application of Fuzzy Logic to Classify Community Welfare Levels Aditra; Sumijan; Sovia, Rini
Journal of Computer Scine and Information Technology Volume 10 Issue 3 (2024): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v10i3.104

Abstract

Information regarding family welfare does not only affect family members, but also influences the success of government, including village government. Therefore, information regarding the level of family welfare is needed to monitor the progress of development programs that have been carried out. The fuzzy logic of the Tahani model is one method that can be applied to classify things. The aim of this research is to classify the level of welfare of families as potential recipients of assistance based on population data held by the Mentawai Social Service & P3A. This research was processed using Fuzzy Tahani logic. Fuzzy Tahani is an optimization algorithm that can be used to support decisions by utilizing relational databases. Based on the research results obtained, fuzzy logic with the Tahani model can be used to process family data in accordance with indicators of family welfare levels by providing output in the form of family classification. It's just that the application of the Tahani model should be done on a single rule search function, not to process all the rules using a Tahani query to produce a family classification
Analisis Data Mining dengan Metode K-Means Clustering Dalam Pengelompokan Penggunaan Alat Kontrasepsi Rahmad, Rahmad; Defit, Sarjon; Sovia, Rini
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.750

Abstract

Family Planning (KB) is a strategic government effort to suppress population growth and improve the quality of life. The availability of various types of contraceptives can delay unwanted pregnancies, including in women facing increased pregnancy risks. Based on this, this study aims to cluster contraceptive use. The K-Means Clustering method is an unsupervised learning algorithm used to group data into several clusters based on similar characteristics. This algorithm works by minimizing the distance between the data and the cluster center (centroid). The advantages of K-Means are its simplicity and speed in processing large data. This research variable uses data from the 2024 Family Data Collection of the BKKBN Representative Office of West Sumatra Province in West Pasaman Regency. Based on the application of the K-Means Clustering method to the contraceptive use data, the grouping is obtained into three clusters: low use of MKJP contraceptives, moderate use of MKJP contraceptives, and high use of MKJP contraceptives. This study contributes in the form of a data mining-based analysis model that is able to group contraceptive use patterns in a more structured and objective manner. By applying the K-Means Clustering method, this study produces information that can be used to identify the characteristics of each user group, so that relevant agencies can design more targeted contraceptive counseling and distribution strategies.
Deteksi Pelanggaran Tata Tertib Siswa Sistem Cerdas Menggunakan Face Recognition dengan Metode Convolutional Neural Network Syafril, Syafril; Yuhandri, Yuhandri; Sovia, Rini
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.753

Abstract

Student disciplinary violations are a social problem increasingly common in schools and can negatively impact students' academic and moral development. This phenomenon requires an effective identification system so that prevention and mitigation efforts can be carried out quickly and accurately. This research aims to develop a student face detection system based on Digital Image Processing (DIP) technology that functions to identify and classify adolescent disciplinary violations. The designed system utilizes a camera as an image acquisition device, then processes it to detect the presence of student faces in real-time. The face detection process is carried out using the Haar Cascade Viola-Jones method, which is known to be able to recognize faces with high speed and accuracy. Once a face is detected, the system continues the analysis process using the Convolutional Neural Network (CNN) method to classify facial expressions and behavioral patterns that could potentially indicate violations. The integration between Haar Cascade and CNN allows the system to work efficiently in identifying signs of negative behavior based on visual data. System testing shows satisfactory results, with a high level of facial detection accuracy and fairly reliable behavior classification capabilities. This technology has the potential to be used as a monitoring tool in the school environment, allowing teachers and school management to quickly identify students who need special attention. With the implementation of this system, it is hoped that schools will be able to provide timely guidance, prevent the escalation of deviant behavior, and create a more conducive learning environment. The use of digital image processing-based technology for detecting and classifying student behavior is a relevant innovation in the modern education era, while also supporting efforts to prevent juvenile disciplinary violations through a systematic and measurable approach.
Prediksi Jumlah Kebutuhan Biji Kopi Berdasarkan Pola Konsumsi Konsumen dengan Algoritma Apriori Sutri, Ridwan; Hendrik, Billy; Sovia, Rini
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.757

Abstract

Coffee bean prediction is needed for optimal inventory management to maintain efficiency. This data grouping is taken from customer shopping consumption patterns. Based on the research aims to predict the amount of coffee bean needs based on consumer consumption patterns by applying the Apriori algorithm. Utilization of processed transaction data can provide what steps should be taken in the future. Based on this, this study aims to predict the amount of coffee bean needs based on consumer consumption patterns with the Apriori algorithm. The Apriori algorithm forms association rules based on a combination of data indicators used. These data indicators are sourced from Freehand Coffee. Based on the use of the Apriori algorithm in predicting coffee bean needs based on consumer consumption patterns, the results showed that the Apriori algorithm is able to provide product recommendations in the form of associative or consumer transaction patterns by collecting transaction data and then experimenting with existing data indicators. The contribution of this research can help Freehand Coffee to estimate coffee bean needs and optimize stock management, this research also helps in selecting drinks based on consumer consumption.
Analisis Kepuasan Masyarakat Terhadap Proses Pengurusan Sertipikat Analog Ke Elektronik Menggunakan Metode Naïve Bayes Al-Arrafi, Muhammad Ikhsan; Sovia, Rini; Ramadhanu, Agung
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.758

Abstract

The certificate media conversion program from analog to electronic implemented by the Ministry of ATR/BPN in Sejati Village requires evaluation to ensure its effectiveness. The main problem faced is the limited use of quantitative, data-driven analysis in identifying the factors that influence public satisfaction. This study aims to analyze the level of public satisfaction using the Naïve Bayes method to classify and predict the influence of related variables. Data were obtained from 250 respondents through questionnaires based on digital public service indicators, covering demographic variables, perceived benefits, obstacles, support, service speed, and procedural simplicity. The results show that the level of public satisfaction is in the high category, with procedural simplicity and service speed proven to be the most significant variables influencing satisfaction prediction. The Naïve Bayes model achieved an accuracy of 94%, demonstrating its effectiveness in predicting satisfaction levels. These findings serve as a basis for improving policies and strategies to enhance the quality of digital public services, particularly in the implementation of electronic certificate media conversion in the future.
Co-Authors Adinda Syalsabila Aditra Agus Salim, David Al-arrafi, Muhammad Ikhsan Awal, Hasri Borianto, B Chairunnissa Deliva Akbar, Syifa Devia Kartika Dila, Rahmah Effendy, Geraldo Revanska Elmi Rahmawati, Elmi Erlanda, Hadrian Fana, Wulan Stau Fatimah, Noor Gema, Rima Liana Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo, Gunadi Gusriva, Revi Hadi, Aulia Fitrul Hadiyanto, Tegas Harnaranda, Jefri Hartika Zain, Ruri Hartika Hendri Irawan Hendrik, Billy Huda, Ramzil Islam, Md Ataul Khomsi, Ahmad Lubis, Fitri Amelia Sari maha rani Nabila, Tuti Nurdiansyah, Ali Nursyahrina Permana, Randi Permana, Randy Putra, Kharisma Utama Rahmad Rahmad Rahman, Muhammad Aidil Rahman, Zumardi Rais, Edo Rinaldi Ramadhanu, Agung Renaldi, Encik Yoega Ricki Ardiansyah, Ricki Ridwan Sutri Rinaldi Chan, Fajri Riyan Saputra Riyan Saputra, Riyan Roza, Yesi Betriana Rozakh, Muhammad Sandawa, Hoka Muhgrah Saputra, Randy Sarjon Defit Selfi Melisa Siregar, Diffri Sumijan Sumijan Sumijan Sutri, Ridwan Syafril Syafril Syaiffullah, Afif Tika Christy Wahyudi, Anggy Wedo, Mhd Wirdawati, Wira Yanti, Rahma Yasmin, Nabilla Yenila, Firna Yuhandri Yuhandri Yuhandri, Yuhandri Yuhandri, Y