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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 JINAV: Journal of Information and Visualization 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 Journal Serambi Engineering (JSE) Jurnal Elektro dan Teknologi Informasi Bulletin of Engineering Science, Technology and Industry
Zara Yunizar
Universitas Malikussaleh
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Journal : International Journal of Engineering, Science and Information Technology

 1 
News Popularity Prediction in West Sumatera Using Autoregressive Integrated Moving Average Aminsyah, Ansharulhaq; Nurdin, Nurdin; Yunizar, Zara
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.615

Abstract

The increasing public interest in reading online news is undoubtedly a challenge for news portals as online news providers. Therefore, this research was conducted to predict news popularity in West Sumatra through the FajarSumbar.com news portal using the Autoregressive Integrated Moving Average (ARIMA) model. This research aims to develop a forecasting model that can assist in estimating the popularity of each news category so that news portals can devise more effective content strategies. The data used in this study includes the number of monthly news impressions from March 2021 to June 2024, which are grouped into various categories such as Religion Culture, Industrial Economics, Criminal Law, etc. Using the ARIMA method, which can handle time series data and overcome data non-stationarity problems through differencing and the use of grid search in optimization to find the best parameters based on the lowest evaluation metric. The results show that the ARIMA model can provide reasonably accurate predictions, although the level of accuracy varies between categories. The Mean Absolute Percentage Error (MAPE) values obtained are as follows: Religion Culture 26%, Industrial Economy 29%, Criminal Law 29%, Health 40%, Sports 38%, Tourism Entertainment 26%, Education 27%, Government Politics 31%, Social Environment 27%, and Technology 51%. The Technology and Health news categories show higher error rates than others, while Religion Culture and Tourism Entertainment have better accuracy rates. Thus, the ARIMA model can be used to predict future trends in news popularity, helping editors plan content strategies that are more relevant and interesting to readers. However, improvements are needed for news categories that have high variability.
Application of the Internet of Things in Monitoring and Controlling Water Quality of Goldfish in Aquariums Mahara, Sabda; Yunizar, Zara; Nunsina, Nunsina
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.763

Abstract

Caring for ornamental fish in aquariums often presents significant challenges, primarily when maintenance relies on manual methods. Owners may face difficulties when they are unavailable to feed the fish or clean the aquarium, potentially compromising the health and well-being of the ornamental fish. Consistent water quality monitoring is critical to maintaining a stable and healthy environment for these aquatic creatures. To address these issues, this study developed an Internet of Things (IoT)-based system to monitor water conditions in real-time via an internet connection. By leveraging IoT technology, the system allows owners to access real-time data and remotely control aquarium parameters through a user-friendly interface, providing convenience and improved management. The system monitors essential water parameters, including temperature and pH levels, to sustain the fish's health and ensure the aquarium ecosystem's stability. Testing was conducted over two days, with data recorded at fifteen-minute intervals each day, and the results demonstrated that the system effectively monitored these parameters with sufficient accuracy. The collected data is presented in an intuitive table interface, making it easy for users to analyze trends and respond promptly to any irregularities. Additionally, the system features automatic actuator controls that allow necessary adjustments without requiring constant user involvement, significantly simplifying aquarium maintenance. The accompanying monitoring website further enhances usability by enabling users to check and manage water quality anywhere and anytime, providing flexibility and control. These features collectively ensure a more effective, efficient, and sustainable approach to ornamental fish care, ultimately improving aquarium management and fish well-being.
Fundamental Analysis in Choosing Altcoins in Cryptocurrency With Preference Selection Index Method Ritonga, Huan Margana; Yunizar, Zara; Aidilof, Hafizh Al Kautsar
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.848

Abstract

Cryptocurrency has become one of the most intriguing topics in finance and technology in recent years. With the growing prominence of Bitcoin, the rise of altcoins (alternative cryptocurrencies) also demonstrates significant potential within the cryptocurrency market. Altcoins, which include all cryptocurrencies other than Bitcoin, offer diverse functionalities and use cases, ranging from smart contracts to decentralized finance (DeFi) applications. This thesis identifies the altcoin options with the best investment opportunities and the highest growth potential. The study employs the Preference Selection Index (PSI) method, a multi-criteria decision-making approach that evaluates alternatives based on specific preferences and criteria. This method is particularly suitable for assessing complex investment decisions involving multiple variables, such as market capitalization, technological innovation, and utility. By applying PSI, investors can decide which altcoins will likely yield substantial returns. A web-based platform has been developed as part of this research to simplify selecting promising altcoins. This platform enables users to evaluate options based on predefined criteria, such as market trends, project objectives, and development team credibility. The accessibility of this tool empowers users—whether novice or experienced investors—to navigate the dynamic cryptocurrency market more effectively. Altcoins provide a unique opportunity for diversification in investment portfolios. Unlike Bitcoin, which is often viewed as a store of value, many altcoins are designed with specific purposes and innovative features. For instance, Ethereum introduced smart contracts that revolutionized decentralized applications, while other altcoins focus on scalability or niche markets like the Internet of Things (IoT). However, investing in altcoins also comes with challenges like high market volatility, security risks, and regulatory uncertainties. Therefore, thorough research and strategic planning are essential for minimizing risks while maximizing returns in this rapidly evolving sector.
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.
Evaluating the Quality of Agglomerative Hierarchical Clustering on Crime Data in Indonesia Rizkya, Dini Dara; Retno, Sujacka; Yunizar, Zara
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.863

Abstract

This study evallualtes the quallity of ALgglomeraltive Hieralrchicall Clustering with single linkalge, complete linkalge, alveralge linkalge, alnd walrd linkalge on the daltalset of the number of criminall calses in Indonesial (20ll0ll0ll-20ll23). The analysis compares clustering performance on the original and normalized datasets using the Davies-Bouldin Index (DBI), Silhouette Score (SS), Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), and Callinski-Harabasz Index (CH). The results showed that Ward Linkage provided the best clustering results, with the highest CH increasing from 65.826 to 66.873, clear cluster separation, and a stable structure (NMI = 0.5855, ARI = 0.6298). Single Linkage experienced a chaining effect, although it showed improvement in DBI from 0l.1793 to 0l.1765 and SS from 0l.6271 to 0l.640l0l, with NMI and ARI stable at 0l.4537 and 0l.5865, but CH decreased from 21.731 to 21.0l72. Complete Linkage was too aggressive in separating the data, shown by an increase in DBI from 0.5327 to 0.7116 and a decrease in SS from 0.6336 to 0.5830, although CH increased from 64.244 to 66.873. Average Linkage showed stable results, with NMI = 0l.6481 and ARI = 0l.7993 remaining, but a slight decrease in DBI from 0l.3874 to 0l.40l91, SS from 0l.6839 to 0l.6825, and CH from 42.358 to 40l.251. Data normalization generally helps to improve clustering quality by reducing the influence of feature scale differences. Several metrics showed improved cluster separation on normalized data, although the impact varied depending on the linkage method. Overall, Ward Linkage with normalization is recommended as the best method to produce more accurate clustering in Indonesia's crime data analysis. 
"WASTEAPP" Application Based on Android for Household Waste Self-Tracking Yunizar, Zara; Savira Ersa, Nanda; Ardian, Zalfie; Rusnani, Rusnani; Maulana Helmi, Fathan
International Journal of Engineering, Science and Information Technology Vol 2, No 4 (2022)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v2i4.327

Abstract

Rapid population growth has both a positive and a negative impact, such as an increase in household waste generation. This also becomes a new chain of problems in other aspects, such as health, flood, air pollution, and environmental pollution. To overcome this problem, an integrated waste management system is needed. Household waste, especially inorganic waste, was valuable. The application was developed to promote a new paradigm; waste is a new source of money. As well as help run a circular economy in the waste recycling industry. The application to be built is "WasteApp", an independent household waste management application that can track and help manage it. This application is built using the Android operating system to make it a user-friendly tool. An application trial was conducted in Geulanggang Baro village, Bireuen district involving the operator, local government, and community. This location was chosen because they already have informal waste management, and it is easier to maximize by information technology intervention. The final result of this research is the “WasteApp” application based on android to manage household waste independently. The community, operator, and local government can work waste independently and systematically through this system. This application contains several menus, including the Home menu, login, register, and the main menu, namely the waste sell history and selling waste, as well as educational articles that contain how to manage waste, from sorting waste to the recycling process. Community participation was needed to increase to full implementation of good waste management.
Co-Authors ,, Iqbal ,, Maulidasari ,, Zulaifani ., Yulisma Agil, Helvina 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 Tarigan, Tasya Amelia Taufiq Taufiq Tjut Adek, Rizal Wahyu Fuadi Yanti, Winda Yesy Afrillia Zalfie Ardian Zulsuhendra, Edi