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All Journal SAMUDERA Infotech Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL PENDIDIKAN TAMBUSAI Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset Komputer) Jurnal Informasi dan Teknologi Jurnal Sosial Humaniora Sigli Jatilima : Jurnal Multimedia Dan Teknologi Informasi Infotek : Jurnal Informatika dan Teknologi Jurnal Teknik Informatika (JUTIF) ARRUS Journal of Engineering and Technology Instal : Jurnal Komputer Jurnal Ilmu Komputer dan Informatika TECHSI - Jurnal Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Journal of Information Technology (JINTECH) Journal Of Artificial Intelligence And Software Engineering Jurnal Malikussaleh Mengabdi Journal of Advanced Computer Knowledge and Algorithms Jurnal Solusi Masyarakat (JSM) INOVTEK Polbeng - Seri Informatika JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Teewan Journal Solutions
Asrianda Asrianda
Teknik Informatika Universitas Malikussaleh
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Sentiment Analysis of Public Comments on X Social Media Related to Israeli Product Boycotts Using The Long Short-Term Memory (LSTM) Method Panggabean, Pitra Rahmadani; Asrianda, Asrianda; Aidilof, Hafizh Al-Kausar
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.v9i3.9458

Abstract

The boycott of Israeli products is a widely discussed issue on social media, particularly on X. This study aims to analyze public sentiment regarding the boycott using the Long Short-Term Memory (LSTM) method. Data was collected via the X API, resulting in 800 comments after cleaning and removing duplicates from initially 980 crawled datasets. LSTM was chosen for this analysis due to its superior ability to process sequential data like text and effectively capture long-term dependencies in natural language, which is crucial for accurate sentiment classification. Data was processed through preprocessing steps, sentiment labeling, and Term Frequency-Inverse Document Frequency (TF-IDF) weighting before being fed into the LSTM model. Sentiment was classified into three categories: positive, negative, and neutral. Model evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The results show that the LSTM model achieved an accuracy of 80.62%, with negative sentiment dominating, followed by neutral and positive. This study demonstrates that the LSTM method effectively classifies public sentiment and can be applied to inform public policy decisions, map public opinion trends, and monitor responses to foreign policy issues related to the Israeli-Palestinian conflict.
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.
Pendapatan Masyarakat Disekitar Kampus dengan Adanya Mahasiswa Menggunakan Fuzzy Asrianda, Asrianda
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 1 No. 1 (2017): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2017
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v1i1.250

Abstract

Bertambahnya permintaan mahasiswa atas kebutuhan makan sehari-hari,berkembangnya usaha warung nasi di sekitas kampus Universitas Malikussalehmemiliki keterkaitan erat dengan konsentrasi konsumen secara nyata berdomilisidan memiliki aktivitas rutin di sekitar kampus, tentunya lebih memilihmembelanjakan uangnya pada warung yang berdomilisi disekitar kampus daripada membelanjakan uangnya pada warung yang jauh letaknya dari area kampus.Bagi masyarakat justru memberikan motivasi sendiri untuk membuka usahadalam mendapatkan keuntungan yang banyak sehingga pendapatan masyarakatakan meningkat tajam. Dilihat secara mendalam, keberadaan usaha masyarakat disekitar kampus belum sampai pada tingkat usaha yang balance. Penelitian inimenggunakan metode fuzzy tsukamoto untuk menyelesaikan pendapatanmasyarakat di sekitar kampus. Metode tsukamoto direpresentasikan dengan suatuhimpunan fuzzy dengan fungsi keanggotaan yang menonton. Fuzzi tsukamotomenentukan pengaruh besarnya modal berpengaruh terhadap pendapatmasyarakat di sekitar kampus, juga dapat menentukan luasnya dan fasilitas yangada di usaha masyarakat berpengaruh terhadap pendapatan masyarakat.Membangun sistem fuzzy guna menentukan pengaruh pendapatan masyarakat disekitar kampus dengan menggunakan metode fuzzy tsukamoto.Kata kunci : kampus,masyarakat, pendapatan, tsukamoto, mahasiswa
Klasterisasi Data Stunting Pada Balita Di Puskesmas Xyz Dengan Menggunakan Metode Mixture Modelling Delianda, Anggun; Asrianda, Asrianda; Fitri, Zahratul
JURIKOM (Jurnal Riset Komputer) Vol 12, No 3 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i3.8580

Abstract

This research is motivated by the high prevalence of stunting in Indonesia, reflecting nutritional imbalances in early childhood. To address this issue, an information technology approach is employed to identify at-risk infant groups. The analyzed data consists of anthropometric information, including height, weight, and age of infants, collected from the Peusangan Health Center. The applied method is the Gaussian Mixture Model (GMM) with the Expectation-Maximization algorithm to cluster the data into two groups: "Potential Stunting" and "Not Stunting." The research results indicate that several Posyandu and villages have notably high potential stunting rates, such as Posyandu Bungong Seulanga (141 infants) and Pante Gajah village (116 infants), with a higher prevalence among male infants (34.67%) and those aged 52–60 months (24.18%). Model evaluation using a confusion matrix on 1,465 data points showed a True Positive of 958 (65.36%), False Negative of 4 (0.27%), False Positive of 503 (34.33%), and True Negative of 0 (0%), with an accuracy of 65.36% and an error rate of 34.64%. However, a previous accuracy test on 1,665 data points only achieved 34.55%, indicating unsatisfactory individual prediction performance. In conclusion, Mixture Modelling is effective for clustering and identifying at-risk groups but lacks accuracy in individual predictions, with a bias toward the "Potential Stunting" class that requires improvement in future research.
Stunting Risk Detection and Food Recommendation via Maternal Diagnosis Using the CF Method Kautsar, Al; Asrianda, Asrianda; Afrillia, Yesy
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.v9i4.9949

Abstract

Stunting in children often stems from maternal health conditions during pregnancy. This study aims to develop an intelligent rule-based IF–THEN system using the Certainty Factor method as a decision-support tool for the early detection of stunting risk factors. The detection is performed indirectly by diagnosing maternal health conditions during pregnancy. The knowledge base was constructed through interviews with obstetricians and nutritionists, encompassing 20 symptoms categorized into three primary conditions namely Chronic Energy Deficiency (CED), anemia, and preeclampsia. A total of 119 pregnant women from 11 villages in Muara Satu District participated as respondents. Implementation results revealed that among the respondents, 20 were identified with CED, 96 had anemia, and 3 exhibited signs of preeclampsia. Based on Certainty Factor (CF) calculations, the confidence distribution for CED included 2 respondents with CF <50%, 5 respondents within the 50–80% range, and 13 respondents with CF >80%. For anemia, 1 respondent had a CF value <50%, 4 fell within the 50–80% range, and 91 respondents had CF values above 80%. Meanwhile, for preeclampsia, all respondents exceeded the 50% CF threshold, with 1 respondent in the 50–80% range and 2 respondents >80%. In addition to diagnosis, the system provides tailored meal recommendations (breakfast, lunch, and dinner) based on the identified health conditions. Expert validation indicated a 90% agreement rate. However, results still require confirmation through clinical examinations and consultations to ensure medical accuracy.
Comparative Analysis of the C5.0 Algorithm and Other Machine Learning Models for Early Detection of Multi-Class Heart Disease Mardhatillah, Mardhatillah; Aidilof, Hafizh Al-Kautsar; Aidilof, Asrianda
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.v9i4.9753

Abstract

Cardiovascular diseases represent the leading cause of mortality worldwide, making accurate and early detection a critical factor for effective medical intervention and improved patient prognosis. While machine learning (ML) offers promising tools for predictive diagnostics, many existing studies rely on single-algorithm approaches or less-than-robust validation methods, thereby limiting the generalizability and real-world applicability of their findings.This study aims to conduct a rigorous, head-to-head comparative evaluation of multiple machine learning algorithms for the multi-class classification of heart disease, with the goal of identifying the most effective and reliable model for this complex clinical task.We utilized a private dataset comprising 300 patient medical records, each described by 11 clinically relevant features. To ensure a robust and unbiased evaluation, a stratified 5-fold cross-validation methodology was employed. Five widely-used classification algorithms were evaluated: Naïve Bayes (NB), Logistic Regression (LR), Random Forest (RF), a C5.0-analog Decision Tree (DT), and Support Vector Machine (SVM). Model performance was assessed using standard metrics, including accuracy, precision, recall, and F1-score.The comparative analysis revealed that the Naïve Bayes algorithm delivered superior performance, achieving the highest mean accuracy of 43.33% (±4.22%). It also led in other key metrics with a mean precision of 43.40%, recall of 43.64%, and an F1-score of 41.26%. Other algorithms, such as Logistic Regression (40.67% accuracy) and Random Forest (39.33% accuracy), demonstrated competitive performance but were ultimately surpassed by the Naïve Bayes model in this specific multi-class classification context.This research underscores the critical importance of employing robust validation techniques and comprehensive comparative analyses to identify optimal models for clinical applications. The Naïve Bayes algorithm emerges as a strong candidate for developing a reliable clinical decision support system for the early differentiation of various heart conditions, providing a foundation for future data-driven diagnostic tools.
Perbandingan Multifaktor Evaluation dan Fuzzy Analytic Hierarchy Process pada Kualitas Biji Kopi Meiyanti, Rini; Asrianda, Asrianda; Azmi, Win
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 2 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i2.9741

Abstract

The development of information technology in the agricultural sector is crucial, including in determining coffee bean quality. This research implements a comparison of decision support systems (DSS) using the Multifactor Evaluation Process (MFEP) and Fuzzy Analytic Hierarchy Process (FAHP) methods to assess coffee bean quality based on moisture content, Trase, defects, color, aroma, and bean size. The results show that FAHP has an accuracy of 77%, higher than MFEP with an accuracy of 71%. Thus, FAHP is more effective in determining the farmers with the best coffee beans, thereby helping to improve the economic well-being of farmers and cooperatives.
Silat Perisai Diri sebagai Upaya Penguatan Karakter dan Kesehatan Masyarakat Asrianda, Asrianda; Wibowo, Patmono; Zulfadli, Zulfadli; ZA , Nasrul
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

Abstract

Silat Perisai Diri sebagai aliran Pencak Silat berfungsi sebagai seni bela diri, tetapi menjadi warisan budaya sarat nilai filosofis, spiritual, dan edukatif. Pengabdian masyarakat melalui pelatihan silat Perisai Diri dilaksanakan sebagai upaya pelestarian budaya sekaligus pembinaan generasi muda. Metode kegiatan dilakukan melalui pelatihan terstruktur dengan pendekatan integratif antara aspek fisik, mental, dan karakter. Hasil pelatihan menunjukkan peningkatan signifikan pada kebugaran fisik, ketangguhan mental, disiplin, rasa hormat, dan kemampuan teknis peserta. Kegiatan dilakukan berkontribusi pada penguatan identitas budaya, kohesi sosial, dan peningkatan motivasi remaja untuk terlibat dalam aktivitas positif, termasuk kompetisi bela diri. Dukungan masyarakat sekitar serta respons positif dari peserta menegaskan program dilakukan relevan dan berpotensi berkelanjutan. Silat Perisai Diri terbukti sebagai sarana bela diri, instrumen pendidikan karakter, kesehatan, dan pelestarian budaya dapat diintegrasikan dalam strategi pengembangan masyarakat berkelanjutan. Kkegiatan pengabdian dapat diperluas melalui sinergi dengan lembaga pendidikan formal maupun nonformal, silat Perisai Diri dapat terintegrasi ke dalam kurikulum ekstrakurikuler sekolah maupun program pembinaan pemuda di tingkat komunitas. Pendekatan dilakukan diharapkan tidak hanya memperkuat aspek fisik dan keterampilan bela diri, tetapi membentuk generasi yang memiliki ketangguhan karakter, kepedulian sosial, serta kecintaan terhadap budaya bangsa. Dukungan berkelanjutan pemerintah daerah, organisasi budaya, dan pihak swasta dapat memperkuat ekosistem pelatihan lebih luas, baik dalam bentuk fasilitas, pendanaan, maupun promosi kegiatan. Dengan adanya jejaring kolaborasi yang solid, pengembangan silat Perisai Diri sebagai warisan budaya sekaligus media pembentukan karakter semakin kokoh, memberi dampak nyata bagi ketahanan budaya nasional dan kualitas sumber daya manusia di era globalisasi.
Pandai Silat Tanpa Cedera: Kenaikan Tingkat Menuju Pengembangan Kelatnas Perisai Diri di Aceh Muhammad, Asrianda; Zulfadli, Zulfadli; Wibowo, Patmono
Jurnal Malikussaleh Mengabdi Vol. 1 No. 1 (2022): Jurnal Malikussaleh Mengabdi, April 2022
Publisher : LPPM Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jmm.v1i1.8394

Abstract

Berlatih silat sangatlah baik sekali, karena semua materi yang di ajarkan bersifat mengolah badan, keringat mengeluarkan kotoran-kotoran tubuh melalui liang pori-pori, mengeluarkan energi negatif dan memasukan energi positif. Energi positif ini yang membuat tubuh tetap fit, ringan, bertenaga, sehat, kuat dan energi cadangan yang terkumpul di dalam tubuh menjadikan daya tahan tetap kuat serta memiliki kekebalan terhadap penyakit.  Keberhasilan dari kegiatan ini dapat dilihat dari aspek pengetahuan dan ketrampilan pesilat Kelatnas Perisai Diri. Aspek pengetahuan dilihat dari hasil tes uji gerak yang diberikan sebelum dan sesudah pelaksanaan kegiatan. Sementara aspek ketrampilan dilihat dari kemampuan pesilat dalam mengulangi Kembali gerakan Teknik yang telah diberikan dan dapat Menyusun program latihan. Kelatnas Perissai Diri yang disusun secara sistematis berdasarkan pada pendekatan pola dan kemampuan pesilat dalam menguasai gerakan silat Perisai Diri. Pembelajaran gerakan teknik Perisai Diri diberikan secara urut dan terinci mulai dari pembukaan dan penutup, sehingga adanya keterkaitan antara latihan fisik dan rohani dengan prilaku pesilat Perisai Diri. 
Co-Authors Aidilof, Hafizh Al-Kausar Aidilof, Hafizh Al-Kautsar Amalia, Iklasni Amalia, Wildi Ar Razi Ardi Wirya Indarto Armaya, Devira Yuda Aulia Rachman Azmi, Win Damayanti, Ita Delianda, Anggun Diqti, Fadillah Fauziah Erwanda, Ade Putra Eva Darnila Fadlisyah Fajriana Fajriana, Fajriana Faresya, Natasya Fyanda, Dwi Auji Gunarwan, Gunarwan Herman Fithra Herman Mawengkang Ibnu Khaldun Jange, Beno Jannah, Rauzatul K. M. Nasution , Mahyuddin Kamaruddin Hasan Kautsar, Al Khaidar, Al Kurniawati Lidya Rosnita Maksal Mina Mardhatillah, Mardhatillah Maryana Maulana, OK Muhammad Majid Muhammad Azhari Muhammad Fikry Muhammad Fikry Munadila, Aura Muqarrabin, Khalis Al Mutammimul Ula Nasrul ZA Nasrul ZA, Nasrul Nurdin Nurdin Panggabean, Pitra Rahmadani Patmono Wibowo Poltak Sihombing Pratiwi, Dinda Putri, Sri Raihan Rachmat Triandi Tjahjanto Rahayu, Lolia Lusiana Rahmat Hidayat Rahmat Hidayat Rahmat Triandi Rahmatillah, Siska Yuna Raihansyah, Khananda Rauzatul Jannah Razi, Ar Rini Meiyanti Risawandi, Risawandi Rozzi Kesuma Dinata Saputri, Rifa Andriani Sembiring, Vivi Dista Br Siti Wahyuni Sujacka Retno Teuku Ferynanda Ramadhan Teuku Nabil Muhammad Dhuha Utari, Sylva Putri Wahdana, Aldi Wibowo, Patmono Wildi Amalia Yesy Afrillia ZA , Nasrul Zahratul Fitri, Zahratul Zulfadl, Zulfadl Zulfadli Zulfadli Zulfadli Zulfadli Zulfadli