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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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Penerapan Hybrid Data Mining Menggunakan K-Means Clutering Dan Decision Tree Untuk Klasifikasi Kasus Perceraian Kabupaten Aceh Tengah Fahruddin, Fahruddin; Ula, Munirul; Muthalib, Muchlis Abd
Jurnal Teknik Informatika dan Elektro Vol 7 No 1 (2025): Jurnal Teknik Elektro dan Informatika
Publisher : Universitas Gajah Putih

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55542/jurtie.v7i1.1879

Abstract

Abstrak– perceraian adalah pengakhiran suatu perkawinan karena sesuatu sebab dengan keputusan hakim atas tuntutan dari salah satu pihak atau kedua belah pihak dalam perkawinan. Islam sendiri telah memberikan penjelasan dan definisi bahwa perceraian menurut ahli fikih disebut talak atau furqoh. Untuk saat ini angka kasus perceraian di Kabupaten Aceh Tengah mengalami peningkatan yang sangat signifikan pada tahun 2019 sampai dengan pertengahan tahun 2022, bahkan dari 23 Kabupaten di Provinsi Aceh yaitu Kabupaten Aceh Tengah adalah kasus perceraian tertinggi hingga mencapai 1273 kasus pada pertengahan 2022. Dari 1273 jumlah kasus tersebut perlu adanya penerapan algoritma kombinasi atau yang di sebut dengan Hybrid Data Mining menggunakan metode K-Means Clustering dan Decision Tree di mana metode ini berfungsi untuk mengolah data kasus perceraian sebagai tujuan mengklasifikasikan data kasus perceraian di kabupaten Aceh Tengah. Pengujian klaster di lakukan dengan 3 model klaster yaitu k=2,k=3 dan k4. Untuk mendapatkan data dari hasil klaster maka di lakukan pengujian kinerja davies bouldin maka menghasilkan nilai kinerja klaster dengan k=2 adalah -2,127, untuk nilai davies bouldin kinerja klaster dengan k=3 adalah -1,794, sedangkan nilai davies bouldin kinerja klaster dengan k=3 adalah -1,854. Berdasarkan simpulan diatas maka pada model 2 dengan jumlah k=3 dapat ditentukan klaster yang akan direduksi yaitu klaster dengan keanggotaan terkecil yaitu cluster 2 dengan jumlah data yang direduksi yaitu 59 data, sehingga jumlah dataset hasil reduksi yaitu 1.214 data. Dengan data hasil reduksi maka di uji menggunakan algoritma decision tree dengan komposisi split data 90:10’80:20 dan 70:10. Dengan demikian maka menghasilkan nilai akurasi data sebelum di reduksi dengan data setelah di reduksi dengan demikian nilai rata-rata akurasi untuk klasisfikasi tanpa reduksi adalah 85,96%, presisi 84,71% dan recall 79,36% dan untuk akurasi setelah direduksi adalah 87,90%, presisi 87,22%, dan recall 82,72%. Sehingga dapat disimpulkan bahwa akurasi klasifikasi dataset setelah direduksi lebih tinggi dari akurasi klasifikasi tanpa reduksi. Kata Kunci: data perceraian, hybrid, k-means clustering, Decision Tree. Abstract– Divorce is the termination of a marriage for any reason by a judge's decision based on the demands of one or both parties in the marriage. Islam itself has provided an explanation and definition that according to fiqh experts, divorce is called talak or furqoh. Currently, the number of divorce cases in Central Aceh Regency has increased very significantly from 2019 to mid-2022, In fact, of the 23 districts in Aceh Province, Central Aceh District has the highest number of divorce cases, reaching 1273 cases in mid-2022. Of the 1273 cases, it is necessary to apply a combination algorithm or what is called Hybrid Data Mining using the K-Means Clustering and Decision Tree method, where this method functions to process divorce case data for the purpose of classifying divorce case data in Central Aceh district. Cluster testing was carried out with 3 cluster models, namely k=2, k=3 and k4, To get data from the cluster results, the Davies Bouldin performance test was carried out, resulting in a cluster performance value with k=2 which was -2.127, for the Davies Bouldin value of cluster performance with k=3 is -1.794, while the Davies Bouldin value of cluster performance with k=3 is -1.854. Based on the conclusions above, in model 2 with the number k=3, the cluster that will be reduced can be determined, namely the cluster with the smallest membership, namely cluster 2 with the amount of data reduced, namely 59 data, so that the total dataset resulting from the reduction is 1,214 data. With the reduced data, it was tested using a decision tree algorithm with a data split composition of 90:10'80:20 and 70:10. In this way, the accuracy value of the data before reduction is produced with the data after reduction, so the average value of accuracy for classification without reduction is 85.96%, precision is 84.71% and recall is 79.36% and for accuracy after reduction is 87. .90%, precision 87.22%, and recall 82.72%. So it can be concluded that the classification accuracy of the dataset after reduction is higher than the classification accuracy without reduction. Keywords: divorce data, hybrid, k-means clustering, Decision Tree.
Classification of Hospital Stay Duration for Schizophrenia Patients at RSUD Muyang Kute Using a Combination of C4.5 and Particle Swarm Optimization Putri Agustina Dewi; Munirul Ula; Said Fadlan Anshari
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 2 (2026): Journal of Advanced Computer Knowledge and Algorithms - April 2026 (In Press)
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i2.25930

Abstract

Schizophrenia is a chronic mental disorder that often requires inpatient care, so an increase in the number of patients can lead to limited bed capacity in psychiatric wards. This study aims to classify the length of hospital stay for schizophrenia patients to support room requirement planning at RSUD Muyang Kute using the C4.5 algorithm optimized with Particle Swarm Optimization (PSO). The dataset consists of 657 medical records of inpatient schizophrenia cases from February 2023 to March 2025, categorized into three length-of-stay classes: short (1–5 days), medium (6–10 days), and long (>10 days). The C4.5 algorithm is used to construct a decision tree model based on historical data, while PSO is employed as an optimization method to improve the model configuration. The evaluation uses classification accuracy and Mean Absolute Percentage Error (MAPE) for room demand estimation. The results show that both the C4.5 and C4.5–PSO models achieve similarly high accuracy on the test data, while the manual MAPE calculation for room demand estimation yields a value of 52.66%. In contrast, the MAPE calculated by the system is 0.00% in the test scenario because all classes in the test data are correctly predicted. The web-based decision support system developed using Python and Streamlit is able to automatically provide predictions of length of stay and estimates of the required number of psychiatric beds at RSUD Muyang Kute.
Analisis Performa Voice Recognition Pada Smart Speaker Menggunakan Metode Random Forest Yani, Muhammad; Fikry, Muhammad; Hasibuan, Arnawan; Nurdin; Munirul Ula; Husaini
Jurnal Inotera Vol. 11 No. 1 (2026): January-June 2026
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol11.Iss1.2026.ID654

Abstract

The development of Internet of Things (IoT) and artificial intelligence technology has driven the increasing use of voice user interfaces (VUI) as a more natural form of human-computer interaction. One widely used VUI implementation is voice recognition-based smart speakers. Despite its widespread adoption, voice recognition performance on smart speakers is not necessarily optimal when used in real-world conditions, particularly in far-field scenarios that are influenced by user distance, environmental noise, and system response time. This study aims to analyze and compare the voice recognition performance of Amazon Alexa smart speakers and the Interactive Speaker System as a non-vendor comparison system. Testing was conducted at varying user distances in a non-soundproof room to represent real-world operational conditions.The obtained performance data was analyzed using the Random Forest method as a classification tool due to its ability to handle multivariate data and nonlinear relationships between variables. The results showed that variations in user distance significantly affected the voice recognition performance of both systems, with a tendency for performance to decrease as distance increased. In addition, differences in system architecture characteristics also influenced the level of resilience to environmental conditions. The application of the Random Forest method also enabled the identification of dominant factors that influence the success of voice recognition. This research is expected to provide theoretical contributions in the study of voice recognition performance in far-field scenarios, as well as practical contributions as a basis for consideration in the selection and development of more reliable voice-based interaction systems in real environments.
SURVEI LITERATUR INFORMATION SECURITY INTELLIGENT UNTUK MEMPREDIKSI POTENSI KERUSUHAN MELALUI ANALISA JARINGAN MEDIA SOSIAL Munirul ula
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 3 No. 1 (2019): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2019
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v3i1.6302

Abstract

Kajian ini bertujuan untuk manganalisa literature terdahulu yang berkaitan dengan penerapan Information Security Intelligent (ISI) untuk memprediksi potensi kerusuhan melalui media sosial. Kajian literatur penelitian terdahulu menunjukkan bahwa berbagai platform media sosial di Internet seperti Twitter, Tumblr, Facebook, YouTube, Blog dan forum diskusi disalahgunakan oleh kelompok-kelompok ekstremis untuk menyebarkan kepercayaan dan ideologi mereka. Situs web microblogging populer seperti Twitter digunakan sebagai platform real time untuk berbagi informasi dan komunikasi selama perencanaan dan mobilisasi massa. Penerapan analisa jaringan media sosial untuk memprediksi kejadian kerusuhan adalah area yang telah menarik perhatian beberapa peneliti selama beberapa tahun terakhir. Ada berbagai macam metode yang telah digunakan dalam literatur terkait prediksi kejadian kerusuhan. Dalam jurnal ini, penulis melakukan kajian literatur mengenai semua metode yang ada dan melakukan analisis yang komprehensif untuk memahami situasi, tren dan kesenjangan penelitian. Analisa kajian ini menghasilkan karakterisasi, klasifikasi, dan meta-anlaysis dari puluhan  jurnal untuk mendapatkan pemahaman yang lebih baik tentang literatur tentang potensi kejadian kerusuhan dengan menggunakan metode sosial media intelligent.
KAJIAN LITERATUR PENERAPAN SOCIAL MEDIA NETWORK DAN INFORMATION SECURITY INTELLIGENT UNTUK MENGIDENTIFIKASI POTENSI RADIKALISASI ONLINE Munirul ula
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 3 No. 2 (2019): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2019
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v3i2.6336

Abstract

Kajian literatur penelitian terdahulu menunjukkan bahwa berbagai platform media sosial di Internet seperti Twitter, Tumblr, Facebook, YouTube, Blog dan forum diskusi disalahgunakan oleh kelompok-kelompok ekstremis untuk menyebarkan kepercayaan dan ideologi mereka, mempromosikan radikalisasi, merekrut anggota dan menciptakan komunitas virtual online. Selama lebih dari 10 tahun terakhir penggunaan analisa jaringan media sosial untuk memprediksi dan mengidentifikasi radikalisasi online adalah area yang telah menarik perhatian beberapa peneliti selama 10 tahun terakhir. Ada beberapa algoritma, teknik, dan alat yang telah diusulkan dalam literatur yang ada untuk melawan dan memerangi cyber-ekstrimis. Dalam jurnal ini, penulis melakukan tinjauan literatur dari semua teknik yang ada dan melakukan analisis yang komprehensif untuk memahami keadaan, tren dan kesenjangan penelitian. Dalam jurnal ini dilakukan karakterisasi, klasifikasi, dan meta-anlaysis dari puluhan  jurnal untuk mendapatkan pemahaman yang lebih baik tentang literatur tentang pendeteksian ektrimis melalui sosial media intelligent .
A Comparative Study of Temporal Convolutional Network and Gated Recurrent Unit for Predicting Ethereum Prices Saiful Kiram; Munirul Ula; Kurniawati Kurniawati
Applied Engineering, Innovation, and Technology Vol. 2 No. 1 (2025)
Publisher : MSD Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62777/aeit.v2i1.55

Abstract

This study compares the performance of the Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) models in predicting the price of Ethereum, which is important to support cryptocurrency investment strategies. With the high volatility of the cryptocurrency market, an accurate and reliable prediction model is needed. In this study, Ethereum's daily closing price data over four years was analyzed using TCN and GRU models to evaluate its predictive capabilities. Model accuracy is measured using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE). The results showed that the TCN model excelled in average accuracy with lower MAE and MAPE values, while the GRU model showed excellence in reducing the impact of large errors with smaller MSE values. This reflects TCN's superiority in capturing the overall pattern of price movements, while the GRU is more responsive to short-term price fluctuations. These findings demonstrate the potential of both models in cryptocurrency price forecasting, with their respective advantages. This research provides valuable information for investors and researchers in developing predictive strategies in dynamic financial markets. A combination of TCN and GRU models can also be explored to improve prediction performance in the future.
Firewall Analytics in DNS and SYN Flood Protection on Mikrotik CCR in the North Aceh District Government Nanda Imanda; Dahlan Abdullah; Fajriana Fajriana; Nurdin Nurdin; Munirul Ula
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1791

Abstract

This study investigates the implementation of an analytical firewall on the Mikrotik Cloud Core Router (CCR) device for network protection against Domain Name System (DNS) and Synchronise Flood (SYN Flood attacks in the information technology infrastructure of the North Aceh Regency Government. DNS-based attacks and SYN Flood have demonstrated a significant disruptive capacity for the continuity of electronic public services, illustrating the urgency of robust security protocols on government infrastructure. The study implemented a quantitative-experimental approach, with methodological triangulation in empirical data acquisition through controlled attack simulations, firewall log analysis, and semi-structured interviews with technical personnel. Experiments are designed with variations in attack intensity to evaluate system resilience thresholds, while firewall log analysis facilitates the identification of anomalous patterns through detection algorithms. The analytics process applies parametric evaluation to temporal mitigation metrics, packet processing capacity, and operational implications on network performance, complemented by descriptive statistical analysis that explores data distribution and temporal trends. The results indicate the differential effectiveness of the specific firewall configuration against a specific attack typology, with an empirical determination of optimisation parameters for real-time mitigation. This research contributes to the corpus of knowledge regarding the security of government networks through the derivation of protective models that are adaptive to the operational characteristics of public infrastructure. The findings have substantive implications for cybersecurity policy formulation in the administrative context of local governments, with extensive significance for the implementation of network architectures that are resilient to volumetric attacks and protocol exploitation.
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