Article Per Year (5 Year)
p-Index From 2021 - 2026
5.908
P-Index
This Author published in this journals
All Journal SAMUDERA Infotech Journal 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 Jurnal Teknik Informatika (JUTIF) International Journal of Engineering, Science and Information Technology JUTECH : Journal Education and Technology ARRUS Journal of Engineering and Technology Instal : Jurnal Komputer Jurnal Ilmu Komputer dan Informatika Journal of Renewable Energy, Electrical, and Computer Engineering 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
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Other

Published : 54 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 54 Documents
Search

 1   2   3   4   5 
ANALISIS MODAL SOSIAL DAN PENGARUH KEMISKINAN MASYARAKAT DI KABUPATEN ACEH UTARA Zulfadli, Zulfadli; Asrianda, Asrianda; Fithra, Herman
Jurnal Sosial Humaniora Sigli Vol 7, No 1 (2024): Juni 2024
Publisher : Universitas Jabal Ghafur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47647/jsh.v7i1.2295

Abstract

Ketidakmampuan seseorang dalam merubah nasib selalu diwarnai dengan berubahnya sikap dan prilaku hidup sehari-hari yang mengarah pada budaya miskin, sehingga masyarakat umum beranggapan bahwa masyarakat miskin adalah kelompok manusia malas yang tidak mau kerja keras. Kehidupan masyarakat sesungguhnya saling tergantung satu sama lain dan menyatu dalam keseimbangan, sehingga setiap peran yang ada fungsional bagi masyarakat. Komponen kultural kehidupan masyarakat era modern. Modal sosial yang lemah akan meredupkan semangat gotong-royong, mempengaruhi kemiskinan, dan meningkatkan pengangguran, kriminalitas meningkat dan saling percaya-mempercayai sesama tetangga akan semakin berkurang. Sehingga perekonomian akan terganggu, karena tidak terjaminnya keamanan dalam beraktifitas guna mencari rezeki demi memenuhi kehidupan sehari-hari. Karakteristik kemiskinan atau tingkat kesejahteraan masyarakat di Kabupaten Aceh Utara, secara rata-rata termasuk dalam kategori sedang, dengan kondisi tempat tinggal masyarakat permanen dan pendidikan banyak yang tamatan perguruan tinggi lebih dari 70%.  Karakteristik masyarakat Kabupaten Aceh Utara umumnya berada pada kategori sedang. Hal ini mengidentifikasikan bahwa masyarakat mempunyai modal sosial yang cukup baik. Khususnya modal soail yang berkaitan dengan reciprocity dan trust
Multi-criteria K-nearest neighbor in the classification of eye diseases at Dr. Fauziah Bireuen Hospital asrianda, asrianda; Rosnita, Lidya; Jange, Beno; Zulfadli, Zulfadli
Bahasa Indonesia Vol 15 No 02 (2023): Instal : Jurnal Komputer Periode (Juli-Desember)
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jurnalkomputer.v15i02.168

Abstract

Classification studies and maps each attribute in one of the predetermined classes. K-NN has several drawbacks such as high computational load in conducting training data, large memory when implemented. The selection of distance metrics and data pre-processing does not affect the increase in accuracy, but in this study the Euclidean distance metric is better than Manhattan in increasing accuracy. Finding the optimal number of neighbors varies between different distances, computation takes a long time. High noise for smaller k, the higher the value of k, the lower the accuracy and the smaller the computation time. K odd or even does not affect the high or low accuracy, but does affect the computation time.
Implementation of a Hybrid Fuzzy SAW and Particle Swarm Optimization Algorithm for a Dynamic Laptop Recommendation System Based on User Preferences Amalia, Wildi; Asrianda; Fajriana
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/nqkpbg48

Abstract

Recommendation systems often struggle to balance personalization with fairness, particularly in addressing the marginalization of minority brands caused by data and algorithmic biases. This study tackles that challenge by developing a dynamic laptop recommendation system tailored to user preferences, leveraging a hybrid algorithm that combines Fuzzy Simple Additive Weighting (Fuzzy SAW) and Particle Swarm Optimization (PSO). Fuzzy SAW is employed to manage uncertainties in subjective preferences such as budget and intended use, while PSO dynamically optimizes the weight of each criterion to enhance personalization. Evaluation was conducted using primary data from 27 respondents in Lhokseumawe, Aceh, collected via surveys and interviews, alongside secondary data on laptop specifications retrieved from the Tokopedia API. The resulting match accuracy reached 74.1%, with Asus accounting for 85.0% of the successful recommendations. In contrast, brands like Lenovo and Advan were significantly underrepresented, underscoring the system’s limited sensitivity to minority brands. This research contributes to the field of recommendation systems by empirically demonstrating the trade-off between optimization and fairness, as well as proposing strategies to mitigate algorithmic bias. Practical implications include better-informed user decisions and fairer brand exposure for e-commerce platforms. Future improvements will focus on expanding data sources and refining PSO parameter tuning to better accommodate underrepresented brands.  
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
Konsep Finite State Machine dan implementasinya pada Game Asrianda, Asrianda; Zulfadli, Zulfadli
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 6 No. 1 (2022): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2022
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v6i1.8352

Abstract

Game merupakan  sesuatu yang digunakan untuk bermain yang dimainkan dengan aturan-aturan tertentu. Penerapan FSM banyak digunakan dalam gameuntuk mendapatkan variasi respon NPC (Non Playable Charackter) antar pemain pada sebuah game. Sistem melakukan aksi yang sama pada state sampai sistem menerima event, baik berasal dari perangkat luar atau komponen sistem itusendiri. Setiap state terhubung oleh transisi yang mengarah ke satu state lainnya.  Dalam Game ini proses FSM berjalan dengan mendapatkan aturan jika misiterselesaikan atau quest terpenuhi, maka akan berpindah state ke level berikutnya, dan apabila misi tidak terselesaikan atau quest tidak terpenuhi makan akan tetap pada state awal. NPC diprogram untuk melakukan tugas atau peran tertentu kepada pemain dengan memberikan misi, atau membantu dalam pertempuran atau sekedar berjalan-jalan untuk memberikan ramainya suasana.
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.
Co-Authors Aidilof, Hafizh Al Kautsar Aidilof, Hafizh Al-Kausar Aidilof, Hafizh Al-Kautsar Alvesaldy, Alvin Amalia, Iklasni Amalia, Wildi Ar Razi Ardi Wirya Indarto Armaya, Devira Yuda Asmaul Husna Azmi, Win Damayanti, Ita Delianda, Anggun Dewi, Tursina Diqti, Fadillah Fauziah Erwanda, Ade Putra Eva Darnila Fadlisyah 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 Mardhatillah, Mardhatillah Maryana Maulana, OK Muhammad Majid Muhammad Azhari Muhammad Dhuha, Teuku Nabil Muhammad Fikry Muhammad Fikry Munadila, Aura Muqarrabin, Khalis Al Mutammimul Ula Nasrul ZA Nasrul ZA, Nasrul Panggabean, Pitra Rahmadani Poltak Sihombing Pratiwi, Dinda Putri, Sri Raihan Rachman, Aulia 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 Hajar Siti Wahyuni Sujacka Retno Utari, Sylva Putri Wahdana, Aldi Wibowo, Patmono Yesy Afrillia ZA , Nasrul Zahratul Fitri, Zahratul Zulfadl, Zulfadl Zulfadli Zulfadli Zulfadli Zulfadli Zulfadli