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All Journal Teknika Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Building of Informatics, Technology and Science Vocatech : Vocational Education and Technology Journal JOURNAL OF INFORMATICS AND COMPUTER SCIENCE G-Tech : Jurnal Teknologi Terapan Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) JINAV: Journal of Information and Visualization Rambideun : Jurnal Pengabdian Kepada Masyarakat International Journal of Engineering, Science and Information Technology ARRUS Journal of Engineering and Technology Instal : Jurnal Komputer Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) Jurnal Ilmu Komputer dan Informatika Brilliance: Research of Artificial Intelligence TECHSI - Jurnal Teknik Informatika Jurnal Teknologi Terapan and Sains 4.0 Electronic Journal of Education, Social Economics and Technology MEUSEURAYA : JURNAL PENGABDIAN MASYARAKAT Jurnal Malikussaleh Mengabdi Journal of Advanced Computer Knowledge and Algorithms Jurnal Pengabdian Masyarakat Ilmu Komputer Serambi Engineering JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Lidya Rosnita
Universitas Malikussaleh
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A Random Forest-Based Predictive Model for Student Academic Performance: A Case Study in Indonesian Public High Schools Saputri, Rifa Andriani; Asrianda, Asrianda; Rosnita, Lidya
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9460

Abstract

The rapid advancement of information technology has transformed education by providing tools to accurately predict students' academic performance. This study aims to develop a system for predicting academic achievement using the Random Forest algorithm, with a case study at SMAN 1 Aceh Barat Daya and SMAN 3 Aceh Barat Daya. Data from 632 student report cards for grades X and XI in the second semester of the 2023/2024 academic year were used, covering subjects such as Mathematics, Indonesian Language, and others, divided into 80% training data (506 samples) and 20% test data (136 samples). The research methodology involved data preprocessing, training the Random Forest model using entropy and information gain to construct decision trees, and performance evaluation using metrics such as accuracy, precision, and recall. The implementation resulted in a web-based application using Python and Flask, featuring an interactive interface and decision tree visualization. Testing on 136 test samples achieved an accuracy of 87.40%, with 111 correct predictions, 16 false positives, and 0 false negatives, demonstrating the model's reliability in identifying high-achieving students without missing potential. This research is expected to assist schools in identifying outstanding students, making data-driven decisions, and designing more effective educational strategies.
Sentiment Analysis of Youtube and Gotube Reviews on Google Play Using the Support Vector Machine (SVM) Method in Indonesia Putri, Sri Raihan; Asrianda, Asrianda; Rosnita, Lidya
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9461

Abstract

This research, titled Sentiment Analysis of YouTube and GoTube Reviews on Google Play Using the Support Vector Machine (SVM) Method in Indonesia, analyzes user perceptions of YouTube and GoTube based on Google Play reviews. The study is motivated by the growing popularity of video streaming apps in Indonesia and the limited sentiment analysis research on these platforms. The research collects 1,600 reviews (800 per app) from 2023-2024 using Python’s Scrapy library. The data is split 70% for training and 30% for testing, undergoing text preprocessing (tokenization, stop word removal, stemming), TF-IDF weighting, and SVM classification with an RBF kernel. Evaluation metrics include accuracy, precision, recall, and F1-score, with PCA used for visualization. Results show 94.50% accuracy overall, 97.01% for YouTube, and 92.66% for GoTube. GoTube has higher positive sentiment (385 of 400 test reviews) than YouTube (345 of 400) but lower negative sentiment (15 vs. 55). However, the model exhibits a positive class bias due to data imbalance. The study concludes that SVM effectively detects positive sentiment, but balancing data and exploring non-linear methods could improve negative sentiment detection.
Classification of Stunting Status Using the Naive Bayes Classifier Algorithm with Backward Elimination Feature Selection Pasaribu, Hafni Maya Sari; Abdullah, Dahlan; Rosnita, Lidya
JINAV: Journal of Information and Visualization Vol. 6 No. 1 (2025)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.jinav4100

Abstract

Stunting is one of the major health issues affecting toddlers that can influence their physical growth and developmental progress, ultimately impacting their quality of life. It is characterized by a child’s height being below the standard for their age. To address this issue, a method is needed to classify the stunting status in toddlers. This study aims to classify stunting status in toddlers using the Naive Bayes Classifier algorithm, with feature selection performed using the Backward Elimination method to improve classification accuracy.The dataset used in this research was collected in 2023 from the Lueng Daneun Public Health Center, located in Peusangan Simblah Krueng Subdistrict, Bireun District. The dataset includes several features such as age, gender, family income, height, weight, sanitation, clean water access, and formula milk consumption. The application of the backward elimination feature selection method is intended to identify the most significant and relevant features for the target variable. The Naive Bayes Classifier was implemented using the Python programming language. The analysis results indicated that the remaining feature, namely the sanitation condition, had a significant contribution to the classification process. The dataset consisted of 244 entries, divided into 195 training data and 49 testing data with an 80:20 ratio. The initial classification results showed an accuracy of 77.55%, a precision of 60.00%, a recall of 64.29%, and an F1-score of 62.07%. After feature selection, the accuracy increased to 81.63%, precision to 63.16%, recall to 85.71%, and the F1-score slightly improved to 72.73%. These results indicate that feature selection in the Naive Bayes model demonstrates good performance.
Implementasi Metode WASPAS dalam Menentukan Bidang Keilmuan Perguruan Tinggi Berdasarkan Minat dan Bakat Siswa SMKN 1 Lhokseumawe Pulungan, Fauzi Irham; Safwandi, Safwandi; Rosnita, Lidya
TEKNIKA Vol. 19 No. 2 (2025): Teknika Mei 2025
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.15522494

Abstract

Pemilihan bidang keilmuan di perguruan tinggi merupakan keputusan penting bagi siswa. Penelitian ini menggunakan metode Weighted Aggregated Sum Product Assessment (WASPAS) dalam Sistem Pendukung Keputusan (SPK) untuk menentukan bidang keilmuan perguruan tinggi siswa SMKN 1 Lhokseumawe berdasarkan minat dan bakat yang diperoleh dari angket. WASPAS menggabungkan pendekatan penjumlahan dan perkalian untuk mengolah data dan menghasilkan rekomendasi ke bidang Sains dan Teknologi (SAINTEK) atau Sosial Humaniora (SOSHUM). Kriteria yang di gunakan sebanyak 20 kriteria dan kriteria yang di gunakan berupa 20 pernyataan dari angket minat dan bakat. Setiap kriteria diberi bobot, dan perhitungan nilai Qi digunakan sebagai dasar rekomendasi. Hasil pengujian menunjukkan bahwa sistem mampu memberikan rekomendasi yang sesuai, dengan 123 siswa (61%) direkomendasikan ke bidang SAINTEK dengan nilai Qi terbesar 0,95 dan 77 siswa (39%) ke bidang SOSHUM dengan nilai Qi terkecil 0,49. Penelitian ini membuktikan bahwa metode WASPAS efektif dalam memilih bidang keilmuan perguruan tinggi yang sesuai dengan minat dan bakat mereka, sehingga dapat menjadi acuan dalam pengambilan keputusan akademik.
Peningkatan Nilai Ekonomi Limbah Sekam Padi Melalui Pelatihan Pembuatan Briket Bioarang Ikhwani, Muhammad; Nisa, Fidyatun; Nurfebruary, Nanda Sitti; Rosnita, Lidya; Rachman, Aulia; Azhari, Muhammad
Jurnal Malikussaleh Mengabdi Vol. 4 No. 1 (2025): Jurnal Malikussaleh Mengabdi, April 2025
Publisher : LPPM Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jmm.v4i1.21529

Abstract

Pengabdian masyarakat ini mengangkat isu pemanfaatan limbah sekam padi di Desa Asan Kareung yang selama ini terbuang percuma dan menimbulkan masalah lingkungan. Kegiatan ini dirancang untuk mentransformasi limbah pertanian tersebut menjadi briket bioarang bernilai ekonomi melalui pendekatan teknologi tepat guna berbasis pemberdayaan masyarakat. Secara metodologis, proses produksi meliputi tiga tahap utama: (1) konversi termokimia sekam padi menjadi arang melalui karbonisasi terkendali (300-600°C, 1-2 jam); (2) formulasi adonan dengan komposisi 90% serbuk arang dan 10% perekat pati kanji; serta (3) proses pencetakan menggunakan modifikasi pipa PVC dan pengeringan bertahap. Hasil evaluasi menunjukkan produk briket yang dihasilkan memenuhi spesifikasi bahan bakar alternatif dengan karakteristik densitas 0,85 g/cm³ dan durasi pembakaran 2-3 jam per unit. Tingkat keberhasilan kegiatan ini ditunjukkan dengan 85% peserta mampu mereplikasi proses produksi secara mandiri. Program ini memberikan dampak multidimensional berupa: (1) mitigasi pencemaran lingkungan melalui pengurangan praktik pembakaran limbah terbuka; (2) penciptaan nilai tambah ekonomi; serta (3) peningkatan kapasitas masyarakat dalam pengelolaan limbah terpadu. Keberhasilan implementasi didukung oleh tiga faktor kunci: ketersediaan bahan baku lokal, kesesuaian teknologi dengan kapasitas masyarakat, dan efektivitas metode pelatihan partisipatif. Untuk menjamin keberlanjutan, diperlukan pengembangan lebih lanjut pada aspek kelembagaan kelompok usaha, diversifikasi produk, dan peningkatan kapasitas produksi melalui semi-mekanisasi. Inisiatif ini menjadi bukti empiris penerapan prinsip ekonomi hijau berbasis komunitas yang berpotensi untuk direplikasi di wilayah agraris lainnya. 
IMPLEMENTATION OF A SMART EMERGENCY LAMP HOME PROTOTYPE BASED ON INTERNET OF THINGS (IoT) USING NODEMCU ESP32 MICROCONTROLLER Fajri, Muhammad; Bustami, Bustami; Rosnita, Lidya
Multidiciplinary Output Research For Actual and International Issue (MORFAI) Vol. 4 No. 4 (2024): Multidiciplinary Output Research For Actual and International Issue
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/morfai.v4i4.3546

Abstract

Power outages in residential environments such as boarding houses often disrupt safety and comfort due to the lack of adequate emergency lighting systems. This study aims to develop a Smart Emergency Lamp Home prototype using the Internet of Things (IoT) with a NodeMCU ESP32 microcontroller. The system is designed to automatically activate emergency lighting during power failures and enable real-time monitoring and control via a web-based interface. The research follows an engineering method involving system design, hardware-software integration, and prototype testing. The hardware includes power sensors (INA219 and PZEM-004T), a 12V battery, inverter, relay module, and TFT display, while the software uses Node.js and a PostgreSQL database for web monitoring. Testing results demonstrate the system's high responsiveness, accurate sensor data transmission, efficient power usage (with power factors above 0.94), and stable operation during simulated blackouts. The notification system also worked effectively, providing timely alerts regarding lamp status and battery conditions. This prototype offers a practical, scalable, and low-cost solution for emergency lighting in residential settings and can be further developed with features like solar charging and mobile-based monitoring.
Classification of the Number of Malaria Cases in Asahan Regency Using Random Forest Application Naza Amarianda; Eva Darnila; Lidya Rosnita
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9960

Abstract

This study aims to classify the number of malaria cases in Asahan Regency using the Random Forest method. This method was chosen because it is able to handle data with many and complex variables and reduce the risk of overfitting. Data were collected from the Asahan Regency Health Office. The research stages include data collection, preprocessing, model training, and model evaluation. The dataset used consists of 568 malaria case data from 25 sub-districts. The data is divided into 80% for training and 20% for testing. Of the total data, there are 109 data 19.2% in the low category, 334 data 58.8% in the medium category, and 125 data 22.0% in the high category. This classification aims to assist in mapping the level of malaria risk in the area. In this study, several variables were used for model training, including health centers, sub-districts, age, month, and gender. The results of the analysis showed that the most influential variables were health centers 47.53%, followed by sub-districts 43.77%, age 6.07%, months 2.18%, and gender 0.45%. The Random Forest model built was evaluated using accuracy, precision, recall, and F1-Score metrics. The evaluation results showed that the model was able to classify the number of malaria cases well, with an accuracy value of 0.97. With these results, Random Forest has proven effective as a classification method in malaria cases in Asahan Regency.
Clustering Village Zones Based On Nutritional Status of Toddlers Using The K-Medoids Method Amelia, Ulva; Yunizar, Zara; Rosnita, Lidya
VOCATECH: Vocational Education and Technology Journal Vol 7, No 1 (2025): August
Publisher : Akademi Komunitas Negeri Aceh Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38038/vocatech.v7i1.223

Abstract

AbstractIn order to improve the effectiveness of nutrition intervention planning in the operational area of the Kuta Blang Health Center, this study aims to develop a village zoning model based on the nutritional status of toddlers using the K-Medoids algorithm. The primary data includes the distribution of nutritional statuses (good, overnutrition, undernutrition, severe malnutrition, obesity) from 41 villages collected during the January–December 2023 period. Data were normalized and processed using a web-based system developed in PHP and MySQL. The clustering process resulted in five zones: Green (optimal nutrition), Yellow (within acceptable limits), Orange (requires monitoring), Red (worst condition), and Purple (critical challenges). Field validation showed strong alignment between clustering results and real conditions. This study concludes that the K-Medoids method can accurately group villages based on nutrition data and produce a practical zoning map. The resulting zones allow for more efficient resource allocation and targeted intervention, especially in Red and Purple zones. Future improvements may include incorporating socioeconomic and healthcare access variables for more comprehensive analysis. AbstrakDalam rangka meningkatkan efektivitas perencanaan intervensi gizi di wilayah kerja Puskesmas Kuta Blang, penelitian ini bertujuan untuk mengembangkan model zonasi desa berdasarkan status gizi balita menggunakan algoritma K-Medoids. Data primer meliputi distribusi status gizi balita (gizi baik, gizi lebih, gizi kurang, gizi buruk, obesitas) dari 41 desa yang dikumpulkan selama periode Januari–Desember 2023. Data tersebut dinormalisasi dan diolah dalam sistem berbasis web menggunakan bahasa pemrograman PHP dan database MySQL. Proses klasterisasi menghasilkan lima zona: Hijau (gizi optimal), Kuning (masih dalam batas wajar), Oranye (perlu pemantauan), Merah (terburuk), dan Ungu (tantangan signifikan). Validasi lapangan menunjukkan kesesuaian tinggi antara hasil klasterisasi dan kondisi nyata. Penelitian ini menyimpulkan bahwa metode K-Medoids mampu mengelompokkan desa secara akurat berdasarkan data gizi dan menghasilkan peta zonasi yang aplikatif. Zona yang dihasilkan memungkinkan alokasi sumber daya dan intervensi yang lebih terarah, khususnya pada zona Merah dan Ungu. Perbaikan di masa depan dapat mencakup integrasi variabel sosial ekonomi dan akses layanan kesehatan untuk analisis yang lebih komprehensif
Development of an Expert System to Detect Mental Disorders in Pregnant Women using Forward and Backward Chaining Methods Dela, Monisa; Darnila, Eva; Rosnita, Lidya
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7930

Abstract

Mental health during pregnancy plays a critical role in fetal development and maternal well-being. However, psychological conditions such as depression, stress, and anxiety in pregnant women often go undetected, especially in primary healthcare settings. This research aims to design and develop a web-based expert system capable of diagnosing the mental health conditions of pregnant women using Forward Chaining and Backward Chaining inference techniques. Forward Chaining is applied to infer possible conditions based on reported symptoms, while Backward Chaining is used to validate hypotheses by tracing required supporting symptoms. The system was developed using patient data collected from three health centers in Lhokseumawe City, totaling 500 records with parameters including name, age, gestational age, number of children, and reported complaints. It incorporates 30 symptoms and 9 diagnostic rules to classify the mental condition and its severity.The results indicate that 179 women were diagnosed with depression (mild 107, moderate 33, severe 39), 150 with anxiety (mild 24, moderate 91, severe 35), and 171 with stress (mild 82, moderate 50, severe 39). The system also demonstrates diagnostic probability (e.g., 66.67% in a specific case). Validation using 20 test cases yielded an accuracy of 85%, showing the system performs reliably in aligning symptoms with diagnostic outcomes. This study makes two significant contributions. Practically, it offers a decision-support tool for midwives and general practitioners to perform early mental health screening of pregnant women, especially in regions lacking access to psychiatric specialists. Scientifically, it demonstrates the effectiveness of a hybrid reasoning approach in handling overlapping psychological symptoms and in assessing severity levels, thereby enriching the development of domain-specific expert systems in maternal mental health. In conclusion, this system provides a practical and accessible solution to support early detection and intervention in maternal mental health, ultimately contributing to improved health outcomes for both mothers and their babies.
IMPLEMENTATION OF A SMART EMERGENCY LAMP HOME PROTOTYPE BASED ON INTERNET OF THINGS (IoT) USING NODEMCU ESP32 MICROCONTROLLER Muhammad Fajri; Bustami; Lidya Rosnita
Multidiciplinary Output Research For Actual and International Issue (MORFAI) Vol. 4 No. 4 (2024): Multidiciplinary Output Research For Actual and International Issue
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/morfai.v4i4.3658

Abstract

Power outages in residential environments such as boarding houses often disrupt safety and comfort due to the lack of adequate emergency lighting systems. This study aims to develop a Smart Emergency Lamp Home prototype using the Internet of Things (IoT) with a NodeMCU ESP32 microcontroller. The system is designed to automatically activate emergency lighting during power failures and enable real-time monitoring and control via a web-based interface. The research follows an engineering method involving system design, hardware-software integration, and prototype testing. The hardware includes power sensors (INA219 and PZEM-004T), a 12V battery, inverter, relay module, and TFT display, while the software uses Node.js and a PostgreSQL database for web monitoring. Testing results demonstrate the system's high responsiveness, accurate sensor data transmission, efficient power usage (with power factors above 0.94), and stable operation during simulated blackouts. The notification system also worked effectively, providing timely alerts regarding lamp status and battery conditions. This prototype offers a practical, scalable, and low-cost solution for emergency lighting in residential settings and can be further developed with features like solar charging and mobile-based monitoring.
Co-Authors Afif, Muhammad Athallah Afridah, Rita Aidilof, Hafizh Al Kausar Aidilof, Hafizh Al Kautsar Al Kautsar Aidilof, Hafizh Amelia, Ulva Amir Fauzi Ansyari, Taufik Habib Armaya, Devira Yuda Asrianda Asrianda Azzahra Iskandar, Farah Bancin, Udurta Bustami Bustami Bustami Dahlan Abdullah Deassy Siska Dela, Monisa Dian Putri, Yohana Diana, Mhd. Arief Efendi, Syahril Efendi, Syahril Elma Fitria Ananda Eva Darnila Eva Darnila Fachry Abda El Rahman Fadlisyah Fadlisyah Fasdarsyah Fasdarsyah Fidyatun Nisa Fuadi, Wahyu Furqan, Hafizul Habib Muharry Yusdartono Hafidh Rafif, Teuku Muhammad Hamsi, Widia Harahap, Ilham Taruna Harahap, Lina Mardiana Ikramina ikramina ikramina, Ikramina Jange, Beno Khairul Amna, Khairul Kurniawati Kurniawati Lina Mardiana Harahap Mara Wahyu Alamsyah Pane Micola Azwir, Andrea Muhammad Azhari Muhammad Fajri Muhammad Fajri Muhammad Fikry Muhammad Ikhwani Muhammad Muaz Munauwar Muhammad Muhammad Muhammad Zarlis Muhammad Zarlis, Muhammad Muharry Yusdartono, Habib Mukti Qamal Mulyadi, Rizki Munirul Ula Muzaffar Rigayatsyah Nanda Sitti Nurfebruary Nasution, Wahidatunnisa Naturizal, Rayhan Naza Amarianda Nurfebruary, Nanda Sitti Nurhaliza Bin Aras Nurqamarina Nurul Aula Nurwijayanti Pasaribu, Hafni Maya Sari Pratiwi, Dinda Pulungan, Fauzi Irham Putri, Sri Raihan Rachman, Aulia Rachmat Triandi Tjahjanto Rahmadani Sari, Putri Dwi Rahmat Triandi Rangkuti, Haris Yunanda Rian Kelana Putra Rini Meiyanti Risawandi, Risawandi Rizal Rizal Rizal Rizal Rizal S.Si., M.IT, Rizal Rizky Putra Fhonna Safriana Safriana Safwandi Safwandi, Safwandi Said Fadlan Anshari salamah salamah Samosir, Dini Kairiyah Saputri, Rifa Andriani Siti Maimunah Sujacka Retno Syahputra, M Oriza Ulva Ilyatin Wahyu Fuadi Yesy Afrillia Yunanda Rangkuti, Haris Zalfie Ardian Zara Yunizar Zulfadli Zulfadli