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All Journal International Journal of Electrical and Computer Engineering Jurnal Teknologi Informasi dan Ilmu Komputer POSITIF Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING JTAM (Jurnal Teori dan Aplikasi Matematika) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Sisfokom (Sistem Informasi dan Komputer) METIK JURNAL Vocatech : Vocational Education and Technology Journal JOURNAL OF INFORMATICS AND COMPUTER SCIENCE Rambideun : Jurnal Pengabdian Kepada Masyarakat International Journal of Engineering, Science and Information Technology Jurnal Tika JURNAL ILMIAH GLOBAL EDUCATION Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Jurnal Minfo Polgan (JMP) TECHSI - Jurnal Teknik Informatika Jurnal Teknologi Terapan and Sains 4.0 Jurnal Bangun Abdimas Variasi : Majalah Ilmiah Universitas Almuslim MEUSEURAYA : JURNAL PENGABDIAN MASYARAKAT Journal of Advanced Computer Knowledge and Algorithms Serambi Engineering Jurnal Elektro dan Teknologi Informasi Bulletin of Engineering Science, Technology and Industry
Zara Yunizar
Universitas Malikussaleh
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy 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 Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Penerapan Teknologi Digital dalam Optimalisasi Konsep Ekonomi Sirkular Menuju Green Economy di Desa Blang Asan Zainal, Zara Yunizar; Usrina, Nura; Ersa, Nanda Savira; Hasan, Phadlin; Arif H., Nanda Nan
Jurnal SOLMA Vol. 14 No. 1 (2025)
Publisher : Universitas Muhammadiyah Prof. DR. Hamka (UHAMKA Press)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/solma.v14i1.17301

Abstract

und: Blang Asan Village, Peusangan Subdistrict, Bireuen Regency, faces serious challenges in waste management due to low public awareness and limited waste transportation facilities. To address this issue, the village government, in collaboration with BUMDes Asan Jaya and Asri Waste Bank, introduced the Integrated Waste Management (PST) program, supported by the Environmental and Forestry Office (DLHK) of Bireuen Regency. This program utilizes digital technology through the Beclean application and the development of composters for organic waste management. Method: The main activities include socialization, training, technology implementation, community mentoring, and the operation of an Android-based waste management application integrated with the Waste Bank. One of the technologies implemented in this program is the Beclean application, which aims to reduce the amount of waste sent to final disposal sites by utilizing the application for integrated waste management. The program is designed using the SMCR (Source, Message, Channel, Response) communication model to ensure effective information delivery and implementation. Results: Indicate increased community participation in recycling and a reduction in the volume of waste sent to final disposal sites (TPA). Additionally, it has created new economic value by processing waste into useful products. The program's sustainability is ensured through regular evaluations to maintain its positive impact on the community and support Clean and Healthy Living Behavior (PHBS), including empowering the community to recycle waste into economically valuable products. Conclusions: Through this initiative, it is hoped that a circular economy system can be realized, enhancing economic growth through household waste management while minimizing environmental damage.
Diet Recommendation Application for Diabetes Patients Using the Preference Selection Index Method Siregar, Winda Ramadhani; Yunizar, Zara; Afrillia, Yesy
Journal of Advanced Computer Knowledge and Algorithms Vol. 2 No. 2 (2025): Journal of Advanced Computer Knowledge and Algorithms - April 2025
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v2i2.17810

Abstract

Diabetes mellitus is a chronic condition characterized by elevated blood glucose levels. Effective diet management is crucial for controlling this condition and preventing serious complications. This study aims to develop a meal recommendation application for diabetes patients using the Preference Selection Index (PSI) method. The data used include user identity, health conditions, food preferences, and the nutritional content of meal menus. The PSI implementation process involves several key steps: collecting user data, normalizing nutritional values based on the minimum and maximum values in the database, adjusting the criterion weights according to the user's health conditions and food preferences, and calculating the PSI for each meal menu. The study results show that this application can provide meal recommendations that match the nutritional needs and health conditions of users. From a total of 10 user data analyzed, 50% received "Red Bean Soup with Vegetables" as the best menu, 30% received "Grilled Chicken Breast with Vegetables," and 10% each received "Grilled Chicken with Green Beans" and "Quinoa Salad with Avocado." The conclusion of this study is that the PSI method is effective in helping diabetes patients select an optimal diet, which can assist in better managing their condition and improving their quality of life. Suggestions for future research include increasing the variability of nutritional data, integrating with wearable technology, and developing reminder and education features.
SISTEM INFORMASI DANA DESA BERBASIS WEB MOBILE DI KECAMATAN MAKMUR KABUPATEN BIREUEN Yunizar, Zara
Jurnal Teknologi Terapan and Sains 4.0 Vol 1 No 3 (2020): Jurnal Teknologi Terapan & Sains
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/tts.v1i3.3265

Abstract

Sistem Informasi Dana Desa bertujuan untuk mempermudah masyarakat yang ingin mengelola dana desa biar bisa lebih mudah dan cepat dengan baik. Sehingga diharapkan dapat memberikan kenyamanan dan kemudahan bagi masyarakat dalam mengelola dana desa tersebut. Sistem ini dikembangkan menggunakan bahasa pemograman PHP dan MySQL dan mengunakan database disesuaikan dengan kebutuhan dalam proses perancangan sistem Informsai Dana Desa Berbasis Web Mobile. Di dalam sistem ini terdapat  beberapa sistem informasi serta Perancangannya menggunakan metode terstruktur yang menggunakan DFD dan ERD sebagai alat bantu untuk merancang Sistem Informasi Dana Desa Berbasis Web Mobile Kecamatan Makmur Kabupaten Bireun.  Kata Kunci” Sistem Informasi, Dana Desa, DFD, ERD.
Evaluating GRU Algorithm and Double Moving Average for Predicting USDT Prices: A Case Study 2017-2024 Rizky, Rahmat; ula, Munirul; Yunizar, Zara
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i1.2328

Abstract

The cryptocurrency market is highly volatile, requiring advanced analytical methods for accurate price forecasting. This study evaluates the effectiveness of Gated Recurrent Units (GRU) and Double Moving Average (DMA) in predicting USDT (Tether Coin) prices using historical data from 2017 to 2024, sourced from Investing.com. Implemented in Jupyter Notebook, the research explores the strengths of each method in analyzing market fluctuations and price trends. GRU, a deep learning-based recurrent neural network, processes sequential data using a gating mechanism, making it effective for capturing short-term price dynamics. DMA, in contrast, is a statistical method that filters market noise to identify long-term trends, making it more reliable for stable market conditions. Performance evaluation shows DMA achieving lower errors (MAE: 5.494, MAPE: 0.0339%) than GRU (MAE: 5.984, MAPE: 0.0369%), suggesting higher accuracy for trend-based predictions. However, GRU’s lower RMSE (8.531 vs. 8.715 for DMA) indicates better adaptability to sudden price fluctuations, making it more responsive to volatile markets. A hybrid approach combining GRU and DMA reveals their complementary strengths—DMA’s minimal bias (-0.0013% MPE) supports stable trend analysis, while GRU’s slight positive bias (0.0286% MPE) captures short-term fluctuations. Additionally, a comparison with Long Short-Term Memory (LSTM) demonstrates its superior predictive accuracy, outperforming both GRU (MAE: 5.98, RMSE: 8.53) and DMA (MAE: 5.49, RMSE: 8.72) with the lowest MAE (4.31), MAPE (0.027%), and RMSE (5.64), alongside minimal bias (MPE: 0.007%). This study highlights the need for integrating multiple forecasting techniques in cryptocurrency price prediction. While DMA is well-suited for stable trends and GRU excels in volatile conditions, LSTM outperforms both, reinforcing the effectiveness of deep learning for financial time-series forecasting.
Analisis Aplikasi Diskusi Online Berbasis Mobile Menggunakan Metode Design Thinking Kurnia Amanda, Destiara; Fikry, Muhammad; Yunizar, Zara
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 1 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v9i1.1022

Abstract

The advancement of information technology has accelerated digital transformation in education, including in the area of thesis supervision. However, there remains a lack of digital platforms specifically designed to support interactive communication between students and academic advisors. This research introduces Boardify, a mobile-based application developed to enhance the thesis consultation process for Informatics Engineering students. The study adopts the Design Thinking methodology, comprising five stages: empathize, define, ideate, prototype, and test. Data were obtained through observation and literature analysis, which then informed the creation of user personas, idea development, and user interface design. The application was evaluated using the System Usability Scale (SUS). Testing involving 55 participants resulted in an average SUS score of 75.6, which falls under the “acceptable” category. Validity and reliability tests also confirmed that the questionnaire items were statistically sound. These results demonstrate that Boardify delivers a positive user experience and improves the efficiency of online thesis consultation. The study contributes to the field of educational technology by offering a user-centered mobile consultation solution for academic settings, and opens up opportunities for future research in integrating artificial intelligence or learning analytics features.
Clustering Status Pemberian Imunisasi Dasar Di Dinas Kesehatan Kabupaten Bireuen Menggunakan Metode K-Medoids NinaUlfauza; Zara Yunizar; Fajriana
JETI (Jurnal Elektro dan Teknologi Informasi) Vol. 3 No. 1 (2024): Jurnal Elektro dan Teknologi Informasi: APRIL
Publisher : Program Studi Teknik Elektro Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/jeti.v1i1.386

Abstract

Abstrak— Imunisasi dasar merupakan salah satu upaya dalam mencegah penyakit menular pada anak-anak. Penelitian ini berfokus pada analisis status pemberian imunisasi di Dinas Kesehatan Kabupaten Bireuen. Metode yang digunakan adalah K-Medoids dengan data imunisasi dasar anak usia 0-5 tahun dari tahun 2020 hingga 2022. Hasilnya mengidentifikasi tiga cluster: Selesai, Belum Selesai, dan Tidak Selesai. Aplikasi berbasis web dirancang menggunakan DFD, JavaScript, Python, dan MySQL. Dari hasil penelitian, terlihat perbedaan status pemberian imunisasi dasar di Kabupaten Bireuen pada tahun-tahun tersebut. Informasi ini dapat menjadi dasar untuk merancang strategi peningkatan cakupan imunisasi dasar di wilayah tersebut. Kesimpulan dari penelitian ini memberikan pandangan yang jelas tentang distribusi dan ketersediaan imunisasi dasar, yang dapat membantu Dinas Kesehatan dalam mengoptimalkan upaya pencegahan penyakit melalui imunisasi. Kata kunci: Imunisasi dasar, k-medoids, status pemberian imunisasi, clustering.
Implementation of Support Vector Machine Method with TF-IDF for Sentiment Analysis of the Al-Zaytun Islamic Boarding School Controversy Fardiansyah, T.; Yunizar, Zara; Maryana, Maryana
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.883

Abstract

Al-Zaytun Islamic Boarding School in Indramayu, West Java, has attracted public attention on social media. The previous Eid prayer went viral because men and women stood in the duplicate prayer rows. In addition, several other aspects also drew public attention, such as the Friday prayer call style being different from the usual, introducing Jewish greetings, and allegedly allowing students to commit adultery, with the sin being redeemable for a certain amount of money. These controversies naturally sparked various reactions from the Indonesian public. This study employs the Support Vector Machine (SVM) method combined with Term Frequency-Inverse Document Frequency (TF-IDF) word weighting to evaluate public sentiment regarding various controversies associated with the Al-Zaytun Islamic boarding school. The data used in this research consists of tweets collected through a scraping process using Tweet Harvest with several relevant keywords. The results are analyzed to classify sentiment into three categories: positive, neutral, and hostile. The entire process is carried out systematically to obtain classification results that are both accurate and relevant to the ongoing social phenomena. Therefore, this study aims to implement the Support Vector Machine (SVM) algorithm to classify Twitter user sentiments towards the Al-Zaytun Islamic Boarding School controversy. The research collected 1,018 tweets through a scraping process using Tweet Harvest via Google Collab, with keywords such as "alzaytun," "zaytun," "panji gumilang," and "al-zaytun." The sentiment distribution consisted of 133 positive sentiments, 313 negative sentiments, and 572 neutral sentiments. Based on the classification evaluation results, the Support Vector Machine algorithm achieved an accuracy of 76%, a precision of 78.3%, a recall of 67.6%, and an F1 score of 69.6%.
Expert System For Detecting Soil Fertility Levels for Oil Palm Cultivation Using the Fuzzy Tsukamoto Yanti, Winda; Yunizar, Zara; Afrillia, Yesy
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.884

Abstract

Soil fertility is one of the critical factors that affect the productivity of oil palm plants. Inappropriate soil fertility levels can cause suboptimal plant growth and even crop failure. Low public knowledge about soil fertility is also a significant factor. This research aims to build an expert system that can detect the soil fertility level for oil palm plants using the fuzzy Tsukamoto method. This system uses three main parameters as a reference: soil acidity (pH), soil moisture, and soil texture. The fuzzy Tsukamoto method was chosen because it can handle uncertain data and provide more flexible results. The system was developed web-based using the PHP programming language and MySQL database, and tested on 49 soil data points from the Agricultural Extension Center of Matangkuli District, North Aceh Regency. The system successfully detected soil fertility levels accurately and consistently. Tests were conducted on 49 soil sample data from various villages in Matangkuli District, North Aceh Regency, where soil fertility in the Low category was found in 43 villages with a percentage of 84%, soil fertility in the Medium category was found in 6 villages with a rate of 16% and soil fertility in the High category was not found in any town of Matangkuli District with a percentage of 0% with valid fertility classification results and by expert judgment. With this system, farmers and agricultural extension workers can be helped to make the right decisions regarding the feasibility of land for planting oil palm plants.
ANALISIS SENTIMEN PADA TWITTER TERHADAP APLIKASI MOBILE JKN MENGGUNAKAN METODE NAÏVE BAYES CLASSIFIER Yunizar, Zara; Rusnani, Rusnani; Ardian, Zalfie; Aidilof, Hafizh Al-Kautsar; Maulana, O.K.Muhammad Majid
JOURNAL OF INFORMATICS AND COMPUTER SCIENCE Vol 9, No 2 (2023): Oktober 2023
Publisher : Ubudiyah Indonesia University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33143/jics.v9i2.3261

Abstract

Abstrak- Mobile JKN merupakan aplikasi yang dibuat oleh BPJS Kesehatan untuk memudahkan beberapa masalah administrasi peserta, sehingga peserta tidak perlu datang ke Kantor Cabang karena dapat dilakukan atau diselesaikan dengan aplikasi ini. Tetapi aplikasi ini tidak jarang memiliki beberapa kendala, sehingga menimbulkan penilaian yang kurang baik terhadap pelayanan tersebut. Media sosial Twitter cocok digunakan untuk tempat mengungkapkan perasaan seseorang, membagikan dan mendapatkan informasi terkini, serta komentar atau opini tentang segala hal yang banyak dikenal dan penggunanya pun cukup banyak. Salah satu cara untuk mencari komentar atau opini dari penulis tentang suatu hal, entitas atau subjek tertentu sehingga dapat diklasifikasikan menjadi opini positif, negatif ataupun netral dapat digunakan dengan Analisis Sentimen. Analisis sentimen dapat dilakukan dengan menggunakan Algoritma Naïve Bayes. Naïve Bayes yang nantinya akan mengelompokkan berdasarkan peluang atau probabilitas, dimana dihitung sekumpulan probabilitas dengan menjumlahkan frekuensi dan kombinasi nilai dari dataset yang diberikan. Pada proses implementasinya, dataset yang didapatkan berjumlah 1001 tweet, dengan perbandingan data training dan data testing yaitu 80:20. Penelitian ini menghasilkan 488 tweet positif, netral 193, dan negatif 320. Sedangkan untuk menghitung akurasinya menggunakan confussion matrix dengan akurasi yaitu 69.65%, presisi 62.18%, recall 60.44%.Kata Kunci: Mobile JKN, Twitter, Analisis Sentimen, Naïve Bayes Classifier.Abstract- Mobile JKN is an application created by BPJS Health to facilitate several administrative problems for participants, so that participants do not need to come to the Branch Office because they can be done or resolved with this application. However, this application often has several problems, giving rise to an unfavorable assessment of the service. Twitter social media is suitable for use as a place to express one's feelings, share and get the latest information, as well as comments or opinions about everything that is widely known and has quite a lot of users. One way to look for comments or opinions from writers about a particular thing, entity or subject so that they can be classified into positive, negative or neutral opinions can be used with Sentiment Analysis. Sentiment analysis can be done using the Naïve Bayes Algorithm. Naïve Bayes will then group based on chance or probability, where a set of probabilities is calculated by adding up the frequencies and combinations of values from the given dataset. In the implementation process, the dataset obtained was 1001 tweets, with a ratio of training data and testing data of 80:20. This research produced 488 positive tweets, 193 neutral and 320 negative. Meanwhile, to calculate the accuracy, a confusion matrix was used with an accuracy of 69.65%, precision 62.18%, recall 60.44%.Keywords: Mobile JKN, Twitter, Sentiment Analysis, Naïve Bayes Classifier.
Implementation of The Logistic Regression Algorithm to Analyze Poverty Factors in Aceh Province Mursyidah, Mursyidah; Kesuma Dinata, Rozzi; Yunizar, Zara
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.v9i4.9715

Abstract

Aceh Province continues to face a high poverty rate despite its abundant natural resources. This study aims to analyze the factors influencing poverty status in Aceh Province by applying a binary logistic regression algorithm. The research specifically focuses on an inferential analytical approach to reveal significant relationships among socioeconomic variables. Secondary data were obtained from the Aceh Provincial Statistics Agency (Badan Pusat Statistik/BPS) for the period 2019–2023. Inferential analysis was conducted using the entire dataset through the statsmodels library to identify variables that are statistically significant to poverty status. In addition, a classification approach was implemented using scikit-learn, with a data split between training data (2019–2022) and testing data (2023), yielding an accuracy of 0.70, precision of 0.81, recall of 0.70, F1-score of 0.66, and AUC of 0.69. These findings provide empirical evidence that improving access to education and equitable infrastructure development in densely populated areas can serve as effective policy focuses in efforts to alleviate poverty in Aceh Province.
Co-Authors ,, Iqbal ,, Maulidasari ,, Zulaifani ., Yulisma Aidilof, Hafizh Al Kautsar Aidilof, Hafizh Al-Kautsar Aisah, Sri Purwani Amelia, Ulva Aminsyah, Ansharulhaq Arief Fazillah Arif H., Nanda Nan Arnawan Hasibuan Asran Asran Bariah, Hairul Bustami Bustami Cindy Rahayu Dahlan Abdullah Devi, Salma Dhyra Gibran Alinda Dr M Rajeswari Elma Fitria Ananda ERNAWITA ERNAWITA Ersa, Nanda Savira Eva Darnila Fadlisyah Fadlisyah Fajri, Riyadhul Fajri, Ryadhul Fajriana, Fajriana Fardiansyah, T. Fasdarsyah Fasdarsyah Fatimah Zuhra Fatimah Zuhra Fatimah Zuhra Fuadi, Wahyu Hafidh Rafif, Teuku Muhammad Harahap, Ilham Taruna Hasan, Phadlin HENDRA ZULKIFLI Irshad Ahmad Reshi Johan, T. M. Kartika Kartika Kurnia Amanda, Destiara Lidya Rosnita M. Fauzan M.Cs, Iqbal, Maghfirah Maghfirah Maha, Dedi Torang P Mahara, Sabda Mahendra Febriliansyah Maizuar Maizuar Maryana Maryana, Maryana Maulana Helmi, Fathan Maulana, O.K.Muhammad Majid Melizar Meutia Rahmi Misbahul Jannah Muhammad Daud Muhammad Fikry Muhammad Ikhwani Muhammad Muhammad Muharni Muharni Mukhlis Mukhlis Mukhlis Mulaesyi, Syibbran Munar, Munar Munirul Ula Mursyidah Mursyidah MUTHMAINNAH Muthmainnah Muthmainnah NinaUlfauza NinaUlfauza Nunsina, Nunsina Nur Mauliza Nura Usrina Nurdin Nurdin Nuryawan, Nuryawan Putri, Riska Yolanda Ramadhana Juseva Ridha, Ridha Rini Meiyanti Ritonga, Huan Margana Rizal S.Si., M.IT, Rizal Rizki Suwanda Rizky Almunadiansyah Rizky Putra Fhonna Rizky, Rahmat Rizkya, Dini Dara Rozzi Kesuma Dinata Rusnani Rusnani Rusniati Rusniati Ruwaida Ruwaida Safwandi Safwandi Said Fadlan Anshari Savira Ersa, Nanda Siregar, Winda Ramadhani Sriana, Anis Suci Fitriani, Suci Sujacka Retno Syintia, Icut Taufiq Taufiq Tjut Adek, Rizal Wahyu Fuadi Yanti, Winda Yesy Afrillia Zalfie Ardian Zulsuhendra, Edi