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All Journal SAMUDERA Infotech Journal ITEj (Information Technology Engineering Journals) JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURIKOM (Jurnal Riset Komputer) Jurnal Sosial Humaniora Sigli Jatilima : Jurnal Multimedia Dan Teknologi Informasi International Journal of Engineering, Science and Information 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 Jurnal Malikussaleh Mengabdi Journal of Advanced Computer Knowledge and Algorithms INOVTEK Polbeng - Seri Informatika JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Asrianda Asrianda
Teknik Informatika Universitas Malikussaleh
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Clustering Level of Cigarettes Addiction Among Malikussaleh University Students Using K-Means Method Alvesaldy, Alvin; Asrianda, Asrianda; Razi, Ar
Journal of Renewable Energy, Electrical, and Computer Engineering Vol. 5 No. 1 (2025): March 2025
Publisher : Institute for Research and Community Service, Universitas Malikussaleh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jreece.v5i1.18165

Abstract

Cigarettes are a form of tobacco product produced by rolling dried tobacco leaves into small cylindrical sticks. Cigarettes are usually used for smoking, namely smoking and inhaling the smoke produced when tobacco leaves are burned. Cigarettes generally contain ingredients such as tobacco leaves, which can contain nicotine, an addictive substance that causes dependence. Apart from that, cigarettes also contain various other dangerous chemicals such as tar, carbon monoxide and formaldehyde. The smoke produced when a cigarette is burned creates more than 4,000 chemicals, of which about 70 are known to cause cancer. This research aims to help students at the Faculty of Engineering, Malikussaleh University to help students find out the level of their addiction to cigarettes. This research also gave birth to a grouping system that uses the Python programming language and MySQL as the database. The K-Means Clustering algorithm used in this grouping system states that out of 200 students at the Faculty of Engineering, Malikussaleh University, 28 people are smokers who have a low level of addiction (C1), 77 people have a moderate level of addiction (C2), 55 people have a heavy level of addiction. (C3), 40 people had a very severe level of addiction (C4). This system can be used to determine the level of cigarette addiction among students at the Faculty of Engineering, Malikussaleh University in the future.
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): Juli
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.  
Classification of Family Hope Program Assistance Recipients Using the C4.5 Algorithm with Z-Score Normalization (Case Study in Atu Lintang District) Wahyuni, Siti; Asrianda, Asrianda; Retno, Sujacka
ITEJ (Information Technology Engineering Journals) Vol 10 No 1 (2025): June
Publisher : Pusat Teknologi Informasi dan Pangkalan Data IAIN Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/itej.v10i1.207

Abstract

One of the challenges in distributing social assistance is determining recipients who are truly eligible objectively and efficiently. This study develops a classification system for Family Hope Program (PKH) recipients by utilizing the C4.5 algorithm combined with Z-Score normalization to group citizen data into Eligible or Ineligible categories. The data used came from 551 residents of Atu Lintang District and included attributes such as house status, wall type, toilet facilities, occupation, and income. The research stages started from data preprocessing, attribute normalization, training the model, to evaluating its performance through metric such as accuracy, precision, recall, and F1-score. The evaluation results showed that the model achieved an accuracy of 94%, precision 0.96, recall 0.90, and F1-score 0.93 for the Eligible category. Based on the confusion matrix, the model was able to correctly classify 47 Eligible residents and 57 Ineligible residents. Analysis of the attributes showed that occupation was the most influential feature in the classification process. These results prove that the application of the C4.5 algorithm can be applied effectively to build a decision support system in the distribution of social assistance, and provide accurate and easy-to-understand results. This study also opens up opportunities for improving model performance by adding more data and testing with alternative algorithms going forward.
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
Penentuan Kualitas Sistem Informasi Tugas Akhir Menggunakan Metode McCall Asrianda, Asrianda
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 2 No. 2 (2018): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2018
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v2i2.1017

Abstract

Tugas akhir memberikan kemampuan kepada mahasiswa untuk mengaplikasi teori yang didapatkan di perkuliahan mulai dari mata kuliah dasar sampai dengan matakuliah praktikum.Nilai Tugas Akhir mahasiswa merupakanbagian nilai dari suatu matakuliah yang diwajibkan terhadap mahasiswa.TugasAkhir mempunyai bobot yang cukup besar didalam penilaian, selain itu tugasakhir dapat dijadikan gambaran oleh seorang mahasiswa tentang aplikasi teoriyang dibutuhkan dilapangan.Pelayanan Tugas akhir yang baik dan cepat, adalahsuatu aspek yang harus dipenuhi untuk menunjang keberhasilan dari kegiatantugas akhir mahasiswa.Pengukuran perangkat lunak ada beberapa faktormenunjukkan atribut kualitas produk dilihat dari sudut pandangpengguna.Sedangkan adalah parameter kualitas produk dilihat dari sudutpandang perangkat lunaknya sendiri.Uji kualitas sistem informasi tugas akhirmahasiswa fakutas teknik menggunakan metode McCall dengan melibatkan 25koresponden secara acak.Setelah melakukan pengukuran kualitas denganmenggunakan metode McCall.maka didapatkan hasil bahwa Sistem InformasiTugas Akhir Mahasiswa Fakultas Teknik mendapatkan nilai total kualitas 74.18dengan predikat baik. Kata kunci : tugas akhir, McCall, kualitas, pengukuran, sistem informasi 
Co-Authors Aidilof, Hafizh Al Kautsar Aidilof, Hafizh Al-Kausar Alvesaldy, Alvin Amalia, Wildi Ar Razi Armaya, Devira Yuda Azmi, Win Damayanti, Ita Delianda, Anggun Dewi, Tursina Eva Darnila Fajriana, Fajriana Fyanda, Dwi Auji Gunarwan, Gunarwan Herman Fithra Jange, Beno Jannah, Rauzatul Kautsar, Al Lidya Rosnita Maryana Muhammad Azhari Mutammimul Ula Nasrul ZA Nasrul ZA, Nasrul Panggabean, Pitra Rahmadani Patmono Wibowo Pratiwi, Dinda Putri, Sri Raihan Rachman, Aulia Rachmat Triandi Tjahjanto Rahayu, Lolia Lusiana Rahmat Hidayat Rahmat Hidayat Rahmat Triandi Rauzatul Jannah Razi, Ar Rini Meiyanti Risawandi, Risawandi Rozzi Kesuma Dinata Saputri, Rifa Andriani Siti Wahyuni Sujacka Retno Utari, Sylva Putri Wibowo, Patmono Yesy Afrillia ZA , Nasrul Zahratul Fitri, Zahratul Zulfadl, Zulfadl Zulfadli Zulfadli Zulfadli Zulfadli Zulfadli