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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan Jurnal Ilmiah KOMPUTASI Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Inotera Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah Jurnal Infomedia MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer IJISTECH (International Journal Of Information System & Technology) JOURNAL OF SCIENCE AND SOCIAL RESEARCH JOURNAL OF INFORMATICS AND COMPUTER SCIENCE Jurnal Sistem Komputer dan Informatika (JSON) Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) IJISTECH Rambideun : Jurnal Pengabdian Kepada Masyarakat International Journal of Engineering, Science and Information Technology jeti JURNAL ILMIAH GLOBAL EDUCATION JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Teknik Informatika dan Elektro (JURTIE) TECHSI - Jurnal Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Teknologi Terapan and Sains 4.0 Jurnal Kemitraan Responsif untuk Aksi Inovatif dan Pengabdian Masyarakat Journal Of Artificial Intelligence And Software Engineering Journal of Advanced Computer Knowledge and Algorithms Applied Engineering, Innovation, and Technology Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Munirul Ula
Universitas Malikussaleh
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 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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Internet of Things and Artificial Neural Network Application for Optimizing Spirulina Cultivation with Palm Oil Mill Effluent Ula, Munirul; Fajriana, Fajriana; Ulfah, Julia
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 6, No 1 (2025)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v6i1.22389

Abstract

This study aims to optimize algae biomass production by utilizing Palm Oil Mill Effluent (POME) as a nutrient source, employing Internet of Things (IoT) technology and Artificial Neural Networks (ANN) for predictive modeling and system control. POME, an organic waste from the palm oil industry, was used as an organic liquid fertilizer to enhance the efficiency and sustainability of algae cultivation. The system was designed to monitor and control key environmental parameters such as pH, temperature, salinity, and dissolved oxygen in real-time during a one-month trial in July 2024. ANN-based models were used to predict and adjust environmental conditions, leading to significant improvements in algae growth and resource efficiency. The results indicate that POME can serve as an effective and eco-friendly nutrient source, contributing to both reduced industrial waste and sustainable biomass production. This integrated approach supports circular economy principles and sustainability goals, with potential applications in bioresource production and waste management. Future research will focus on large-scale system testing, optimization for various algae species, and long-term sustainability assessment.
Perbandingan Kinerja Protokol MQTT dan HTTP Dalam Komunikasi Data Internet of Things Fikhri, Aditya Aziz; Ula, Munirul; Sayuti, Muhammad; Taufiq, Taufiq; Nudin, Nurdin
Jurnal Infomedia: Teknik Informatika, Multimedia, dan Jaringan Vol 10, No 1 (2025): Jurnal Infomedia
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jim.v10i1.6733

Abstract

Penelitian ini membandingkan kinerja protokol MQTT dan HTTP dalam sistem komunikasi Internet of Things (IoT), khususnya untuk pemantauan kualitas udara ruang kelas secara real-time. Evaluasi dilakukan menggunakan server virtual machine dengan spesifikasi identik, berdasarkan parameter seperti penggunaan CPU, waktu pengiriman pesan, dan tingkat kehilangan data. MQTT, sebagai protokol ringan dengan model publish-subscribe, menunjukkan kecepatan pengiriman pesan yang jauh lebih tinggi dibandingkan HTTP, terutama pada skenario dengan volume pesan yang besar. Namun, penggunaan CPU pada MQTT meningkat tajam seiring bertambahnya jumlah pesan, dan terjadi kehilangan data yang signifikan hingga 33,8% pada pengiriman 600.000 pesan. Sebaliknya, HTTP yang berbasis model request-response dengan mekanisme multi-proses, mampu menjaga keandalan pengiriman pesan hingga 100%, meskipun waktu pengirimannya jauh lebih lambat. Hasil penelitian ini menunjukkan bahwa MQTT lebih efisien untuk sistem yang membutuhkan kecepatan tinggi dan dapat mentoleransi sebagian kehilangan data, sementara HTTP lebih cocok untuk aplikasi yang menuntut keandalan tinggi dan akurasi data secara penuh. Temuan ini memberikan wawasan penting bagi pengembang dalam memilih protokol komunikasi yang sesuai berdasarkan kebutuhan sistem IoT dan skala implementasinya.
ANALISA DAN DETEKSI KONTEN HOAX PADA MEDIA BERITA INDONESIA MENGGUNAKAN MACHINE LEARNING Ula, Munirul
Jurnal Teknologi Terapan and Sains 4.0 Vol 1 No 2 (2020): Jurnal Teknologi Terapan & Sains
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/tts.v1i2.3263

Abstract

Sekarang  ini konten Hoax yang mengandung informasi tidak benar malah sering kali menjadi konsumsi massal pengguna internet. Hal ini merupakan sesuatu yang buruk karena dapat meningkatkan rasa tidak percaya terhadap berita dan informasi yang ada di internet hingga menimbulkan kebingungan pada masyarakat dalam menentukan informasi mana yang benar. Dalam Penelitian ini, percobaan yang dilakukan bertujuan untuk memilih algoritma terbaik dalam membedakan berita hoax dan berita asli menggunakan metode text mining serta pendekatan dengan machine learning dan  150 artikel berbahasa Indonesia (50 artikel hoax dan 100 artikel asli) sebagai data yang akan digunakan.Penelitian ini akan dimulai dengan tahap preprocessing teks yang terdiri dari tokenizing, case folding, filtering, stopword removal, stemming dan weighting TF-IDF menggunakan penggabungan fitur unigram dan bigram baru kemudian diolah menjadi teks klasifikasi. Hasil dari penelitian ini didapatkan kesimpulan bahwa  algoritma Random Forest memiliki akurasi terbaik dalam mengklasifikasikan berita hoax dan berita asli dibandingkan dengan algoritma Multilayer Perceptron, Naïve Bayes,dan Support Vector Machine dengan nilai akurasi 75.37%. Kata kunci : Klasifikasi,  Berita, Hoax,  Text mining,  Machine learning
Image Feature Extraction for Determining the Ripeness Level OF Oil Palm Fruits Using the K-Nearest Neighbor Algorithm Based on Color Features (Case PTPN IV Aceh Utara) Sudarti, Atrida; Ula, Munirul; Fajriana, F
IJISTECH (International Journal of Information System and Technology) Vol 9, No 1 (2025): The June Edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v9i1.391

Abstract

The availability of oil palm fruits at the appropriate ripeness level is crucial to achieving optimal oil production. Farmers often struggle to accurately determine fruit ripeness, resulting in inconsistent quality and reduced efficiency. This study aims to develop a classification system to determine the ripeness level oil palm fruits using the K-Nerest Neighbor (K-NN) algorithm based on color features extracted from fruits image. Color is a key indicator of maturity and directly influences oil yield. The data was collected through image acquisition and direct observation at the Cot Girek Palm Oil Mill (PKS) of PTPN IV, Aceh Utara. Image preprocessing was carried out to enhance and nomalize the data before feature extraction. The extracted color features were then used to classify the fruits into ripe and unripe categories using the K-NN algorithm. The results show that K-NN successfully classifies the ripeness level of oil palm fruits with an accuracy of 72.80%. This system provides a recommendation for fruit feasibility before processing, helping reduce production losses caused by immature or overripe fruits. Overall, this research contributes to improving decision-making in the palm oil industry through the application of image processing of machine learning techniques.
Image Feature Extraction for Determining the Ripeness Level OF Oil Palm Fruits Using the K-Nearest Neighbor Algorithm Based on Color Features (Case PTPN IV Aceh Utara) Sudarti, Atrida; Ula, Munirul; Fajriana, F
IJISTECH (International Journal of Information System and Technology) Vol 9, No 1 (2025): The June Edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v9i1.391

Abstract

The availability of oil palm fruits at the appropriate ripeness level is crucial to achieving optimal oil production. Farmers often struggle to accurately determine fruit ripeness, resulting in inconsistent quality and reduced efficiency. This study aims to develop a classification system to determine the ripeness level oil palm fruits using the K-Nerest Neighbor (K-NN) algorithm based on color features extracted from fruits image. Color is a key indicator of maturity and directly influences oil yield. The data was collected through image acquisition and direct observation at the Cot Girek Palm Oil Mill (PKS) of PTPN IV, Aceh Utara. Image preprocessing was carried out to enhance and nomalize the data before feature extraction. The extracted color features were then used to classify the fruits into ripe and unripe categories using the K-NN algorithm. The results show that K-NN successfully classifies the ripeness level of oil palm fruits with an accuracy of 72.80%. This system provides a recommendation for fruit feasibility before processing, helping reduce production losses caused by immature or overripe fruits. Overall, this research contributes to improving decision-making in the palm oil industry through the application of image processing of machine learning techniques.
Comparison of the Performance of Fuzzy Tsukamoto and Fuzzy Mamdani in an Internet of Things Based Grape Greenhouse Control System Rusadi, Athirah; Ula, Munirul; Daud, Muhammad; Nurdin, Nurdin; Hasibuan, Arnawan
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): Juni On-Progress
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.6936

Abstract

The application of Internet of Things in agriculture, particularly in grape greenhouses, enables automated environmental control to enhance efficiency and crop yield. This study compares the performance of two fuzzy logic methods, Fuzzy Mamdani and Fuzzy Tsukamoto, in a temperature and humidity control system based on IoT using the DHT22 sensor. The system is designed to automate irrigation via actuators based on sensor data. Performance evaluation was conducted using RMSE, MAE, and standard deviation metrics. The results show that the Tsukamoto method achieved lower RMSE 2.6928, MAE 2.2625, and standard deviation 1.1080 compared to the Mamdani method, which recorded RMSE of 2.9039, MAE of 2.3947, and standard deviation of 1.9268. However, a paired t-test yielded a p-value of 0.0690 0.05, indicating no statistically significant performance difference. Thus, while Fuzzy Tsukamoto appears superior in metrics, both methods are considered equally effective for controlling environmental conditions in grape greenhouses.The application of Internet of Things in agriculture, particularly in grape greenhouses, enables automated environmental control to enhance efficiency and crop yield. This study compares the performance of two fuzzy logic methods, Fuzzy Mamdani and Fuzzy Tsukamoto, in a temperature and humidity control system based on IoT using the DHT22 sensor. The system is designed to automate irrigation via actuators based on sensor data. Performance evaluation was conducted using RMSE, MAE, and standard deviation metrics. The results show that the Tsukamoto method achieved lower RMSE 2.6928, MAE 2.2625, and standard deviation 1.1080 compared to the Mamdani method, which recorded RMSE of 2.9039, MAE of 2.3947, and standard deviation of 1.9268. However, a paired t-test yielded a p-value of 0.0690 0.05, indicating no statistically significant performance difference. Thus, while Fuzzy Tsukamoto appears superior in metrics, both methods are considered equally effective for controlling environmental conditions in grape greenhouses.
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.
Machine Learning to Predict Food Prices in Aceh Province Using the Fuzzy Time Series Method Based on Average Fadillah, Rizky; Ula, Munirul; Suwanda, Rizki
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14649

Abstract

This study aims to develop a food commodity price prediction system based on Fuzzy Time Series (FTS) using average-based methods, with a case study of price data from 2018 to 2023. The system is designed to predict the prices of five main commodities: Super Quality Rice, Fresh Chicken Meat, Fresh Chicken Eggs, Bulk Cooking Oil, and Premium Quality Sugar. The prediction process involves constructing the Universe of Discourse, intervals, and fuzzy logic relations (FLR and FLRG) to model historical price patterns. The results show that this model provides accurate predictions, with the best Mean Absolute Percentage Error (MAPE) value of 0.49% for Super Quality Rice, while MAPE for other commodities ranges from 0.69% to 1.44%. The comparison graph between actual data and prediction results demonstrates consistent pattern alignment, suitable for commodities with both high price fluctuations and stable trends. This system proves effective in projecting future food prices with low error rates, making it a reliable tool to support strategic decision-making in managing food commodity prices during the five-year analysis period.
Vulnerability Risk Assessment Menggunakan Metode Open Web Application Security Project (OWASP) pada E-Learning Perguruan Tinggi Negeri di Aceh Rahman, Ashri Nurhajizah; Ula, Munirul; Nisa, Fidyatun
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 13, No 2 (2025)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v13i2.79172

Abstract

Salah satu aplikasi yang sangat diperlukan saat ini adalah e-learning atau pembelajaran daring. Aplikasi ini membawa banyak manfaat dalam proses belajar mengajar secara online. Selain itu, e-learning juga membantu mengatasi kendala waktu dan ruang dalam pembelajaran. Penelitian ini dilakukan dengan tujuan mengumpulkan informasi mengenai permasalahan keamanan dan kerentanan e-learning yang digunakan di perguruan tinggi negeri, terutama di Aceh, 4 e-learning di Aceh menjadi subjek pengujian. Metode yang digunakan adalah Vulnerability Risk Assessment dengan perhitungan OWASP Risk Rating dan menggunakan alat bantu OWASP Zap. Metodologi ini terbagi menjadi beberapa faktor, seperti Threat Agent Factors, Vulnerability Factors, Technical Impact, dan Business Impact, untuk mengukur tingkat risiko keamanan website. Hasil penelitian menunjukkan bahwa Politeknik Negeri Lhokseumawe (PNL) memiliki kerentanan tertinggi dengan nilai 4,42 dan level risiko medium. Di peringkat kedua adalah Institut Seni Budaya Indonesia Aceh (ISBI Aceh) dengan nilai 4,36 dan level risiko medium. Universitas Malikussaleh (UNIMAL) berada di peringkat ketiga dengan nilai 4,33 dan level risiko medium, sementara Universitas Syiah Kuala (USK) menempati peringkat terakhir dengan nilai 4,29 dan level risiko medium.
Pengukuran Tingkat Kepuasan Pengguna Aplikasi BTNS Mobile Menggunakan Metode Evaluasi Heuristik dan System Usability Scale (SUS) (Studi Kasus: Mahasiswa Universitas Malikussaleh) Dhani, Saniah; Ula, Munirul; Ulva, Ananda Faridhatul
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 13, No 2 (2025)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v13i2.79173

Abstract

Kepuasan pengguna terhadap aplikasi menjadi hal penting dalam memastikan aplikasi memenuhi pengalaman pengguna. Objek penelitian ini adalah BTNS Mobile, dimana aplikasi ini hanya memperoleh rating 3,0 dari 5 bintang di Google Play Store. Terdapat banyak keluhan dan ulasan negatif terhadap BTNS Mobile. Penelitian ini melibatkan 185 responden dan 3 orang pakar serta karyawan bank BTN. Evaluasi heuristik oleh para pakar mengidentifikasi masalah yang signifikan, seperti tombol dan tautan yang tidak sesuai dan desain yang tidak memenuhi standar estetika minimalis. Responden dari jurusan Sistem Informasi dan Teknik Informatika lebih kritis dalam penilaiannya, sedangkan responden dari jurusan lain cenderung memberikan nilai "Masalah kosmetik". Evaluasi oleh bank menunjukkan aplikasi memiliki masalah minor. Skor rata-rata SUS adalah 64, dalam kategori "Marjinal", dengan perbandingan antara jurusan Sistem Informasi/Teknik Informatika (63,2) dan jurusan lain (65,3). Skor kepuasan menurut bank adalah 69,4. Evaluasi heuristik merekomendasikan perbaikan pada beberapa aspek aplikasi, termasuk Visibilitas Status Sistem, Pengenalan Alih-alih Mengingat, Fleksibilitas dan Efisiensi Penggunaan, hingga Bantuan dan Dokumentasi. Rekomendasi untuk perbaikan meliputi perbaikan UI/UX, fitur bantuan, opsi login yang lebih aman, serta perbaikan pada tampilan dasbor, menu transfer, dan pembayaran.
Co-Authors Abdullah, ⁠Dahlan Afif, Muhammad Athallah Afridah, Rita Agustriya, Manda Al-Ghiyats, Said Ananda Faridhatul Ulva Andreansyah, Sabda Arnawan Hasibuan Azzikri, Affan Syafiq Bustami Bustami Cut Agusniar Dahlan Abdullah Dhani, Saniah Dinda, Dinda Fadillah, Rizky Fahruddin Fahruddin Fajriana, F Fajriana, Fajriana Farhiyah, Dara Fasdarsyah Fasdarsyah Fidyatun Nisa Fikhri, Aditya Aziz Fitri, Anisa Amelia Fuddin, Mudhya Hasan Dalimunthe, Amir Husaini Kamaruzzaman, Hilda Zulfira Khalid, M David KURNIAWATI - Kurniawati Kurniawati Lidya Rosnita Lubis, Syahrul Andika Melani, Siska Amelia Muhammad Daud Muhammad Fauzan Muhammad Fikry Muhammad Ikhwanus Muhammad Muhammad Muhammad Yani, Muhammad Mutammimul Ula Muthalib, Muchlis Abd Nadia Saphira Nanda Imanda Nasution, Wahidatunnisa Nurdin Nurdin Nurdin Nurdin Nurdin Nurul Aula Putri Agustina Dewi Putri, Nazirah Allisya Rahman, Ashri Nurhajizah Ridha, Ridha Rini Meiyanti Rizal Rizal Rizal S.Si., M.IT, Rizal Rizki Suwanda Rizky Putra Fhonna Rizky, Rahmat Rozzi Kesuma Dinata Rusadi, Athirah Said Fadlan Anshari Saiful Kiram Salimuddin, Salimuddin Sayed Fachrurrazi Sayuti, Muhammad Siagian, Tania Annisa Sinambela, Reza Syahputra Sitepu, Yuni SariBr Siti Aminah Sudarti, Atrida Sujacka Retno Susanti Susanti Taufiq Taufiq Ulfah, Julia Veri Ilhadi Yasin, Fijri Ahmad Yessi Apprilia Yesy Afrillia Yopy Anfelia Yulisda, Desvina Zailani Mohamed Sidek Zara Yunizar