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All Journal Teknika Sinkron : Jurnal dan Penelitian Teknik Informatika Informatics for Educators and Professional : Journal of Informatics SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan INTECOMS: Journal of Information Technology and Computer Science JURNAL PENDIDIKAN TAMBUSAI Progresif: Jurnal Ilmiah Komputer Jurnal Sains dan Teknologi Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Teknik Elektro dan Komputasi (ELKOM) Community Engagement and Emergence Journal (CEEJ) REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Jurnal Vokasi Informatika (JAVIT) AJAD : Jurnal Pengabdian kepada Masyarakat Jurnal Pendidikan Sains dan Komputer International Journal Software Engineering and Computer Science (IJSECS) Indonesian Journal of Multidisciplinary Science Aptekmas : Jurnal Pengabdian Kepada Masyarakat Malcom: Indonesian Journal of Machine Learning and Computer Science Cross-border CKI On Spot SmartComp Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Pengabdian Nasional (JPN) Indonesia International Journal of Law Social Sciences and Management
Akbar, Yuma
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Penerapan Metode Naïve Bayes untuk Klasifikasi Produk Berdasarkan Kategori Penjualan di Toko Artemist Akbar, Yuma; Qibthiyah, Mariyatul
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 6 No. 3 (2025): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v6i3.1603

Abstract

The implementation of this model offers practical benefits for stock management, promotional planning, and data-driven product strategy decisions, thereby improving operational efficiency for medium-scale retail businesses. The application of data analysis in the retail sector is essential to support accurate and efficient decision-making. This study aims to classify products at Artemist Store into two categories: high demand and low demand, using the Naïve Bayes method. The data used are sales records for one year with a total of 8,106 transactions, which after preprocessing resulted in 148 products. Class labels are determined based on the average sales threshold. The dataset is divided using a stratification scheme of 70% training data (103 products) and 30% test data (45 products). The Naïve Bayes algorithm is implemented in RapidMiner Studio software. The evaluation results on the test data show an accuracy of 93.33%, with 89.29% precision and 100% recall in the high demand class, and 100% precision and 85% recall in the low demand class. These findings prove that Naïve Bayes is effective in identifying products with different levels of consumer interest, while also providing practical benefits in the form of stock management recommendations, promotional planning, and data-driven marketing strategies for medium-scale retailers.
Optimasi Access Control List (ACL) Jaringan dalam Menangkal Akses Ilegal Jaringan Cisco Arif, Sulthan Cendikia; Surapati, Untung; Akbar, Yuma; Hidayat, Aditya Zakaria
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 6 No. 3 (2025): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v6i3.1606

Abstract

This study examines how to block unauthorized access while keeping services available in an enterprise network. The approach combines Access Control Lists (ACLs) allow/deny rules on routers and Policy-Based Routing (PBR), which steers specific traffic without changing the main routing setup. The object of study is a lab simulation with four understandable parts: a central network (head office), an applications/services network, a provider/carrier network, and an external network (internet/partners). The method evaluates three scenarios: baseline, ACL, and ACL + PBR, in a virtual environment using straightforward measurements (ping, traceroute, and rule/route activity logs). Results show the internal subnet is closed in both directions as required; the legitimate path from the central network to the services network remains available and balanced via the provider network; there is no route leakage from the external network to unauthorized areas; and PBR successfully guides specific flows without disrupting the primary path. In conclusion, combining ACL + PBR effectively strengthens security while maintaining service availability, serving as a practical guide for multi-domain enterprise networks.
Pengembangan Sistem Kipas Angin Pintar Berbasis IoT untuk Pemantauan Suhu dan Kelembapan secara Real-Time Arief, Yoga Sofyan; Akbar, Yuma
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 6 No. 3 (2025): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v6i3.1607

Abstract

The increase in indoor temperature and humidity often leads to discomfort and excessive energy consumption in conventional fans operated manually. This study aims to develop a smart fan system based on the Internet of Things (IoT) with adaptive control, capable of monitoring temperature and humidity in real-time and adjusting fan speed automatically or manually via a smartphone application. The system was built using a DHT11 temperature and humidity sensor, a NodeMCU ESP8266 microcontroller, and the Blynk IoT platform as the primary interface. Test results show that the sensor achieved an accuracy of ±0.8°C for temperature and ±3% for humidity, with an average data transmission latency of < 2 seconds and a fan response time of < 1 second. The system operated stably for approximately 1 hours without connection interruptions. By applying adaptive control through hysteresis logic, the system has the potential to reduce energy consumption by dynamically adjusting the fan duty cycle compared to conventional fans. This development contributes to creating an adaptive, comfortable, and energy-efficient indoor environment.
Penerapan Algoritma Naive Bayes dalam Sistem Analisis Sentimen Media Sosial X terhadap Film Agak Laen Aulia, Mutia Dwi; Akbar, Yuma
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 6 No. 3 (2025): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v6i3.1608

Abstract

This study examines the application of the Naïve Bayes algorithm for sentiment analysis on social media platform X (formerly Twitter) regarding the movie Agak Laen. In the digital era, understanding public opinion is highly important, and this film was chosen as a case study due to the large number of circulating reviews. Naïve Bayes was selected for its efficiency in text classification. The research process began with data collection using the TwCommentExport extension, followed by preprocessing to remove noise such as links and punctuation. The cleaned data were manually labeled into positive or negative sentiments. Subsequently, the data were transformed into numerical representations using TF-IDF feature extraction and trained with the Naïve Bayes algorithm. The dataset was divided into 70% training data and 30% testing data to evaluate the model’s performance. The experimental results demonstrated an accuracy of 75.73%. These findings indicate that Naïve Bayes is an effective method for analyzing movie sentiment, although further improvements in data processing or advanced classification techniques are still possible. This research is expected to provide insights into public responses to the film Agak Laen and serve as a reference for the film industry as well as researchers in understanding audience opinions more comprehensively.
Dampak Pengaruh Artificial Intelligence terhadap Orisinalitas Karya Ilustrator Menggunakan Support Vector Machine NST, Silvan; Akbar, Yuma
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 6 No. 3 (2025): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v6i3.1610

Abstract

This study is a case study that investigates how the application of Artificial Intelligence (AI) affects the creative capacity of illustrators in the process of creating visual characters. The research focuses on the use of Stable Diffusion, a machine learning model that utilizes diffusion techniques to generate images, and the Support Vector Machine (SVM) algorithm as a method for classifying and analyzing data. By observing the interaction between illustrators and AI systems, this study aims to assess the extent to which AI influences visual style, creation methods, and the originality of resulting works. Data were collected through interviews, direct observation of the creative process, and comparisons between manually created works and those involving AI. The results indicate that AI holds significant potential to accelerate the design process and expand visual exploration, though it also presents challenges related to authenticity, dependency, and shifts in the illustrator’s role within the creative industry. SVM is used to categorize creators' perspectives on AI based on various parameters, which are then analyzed to identify common patterns. This research is expected to provide valuable insights for creative industry practitioners and academics in utilizing technology wisely without compromising artistic values and originality in illustrative works.
Automatic Purchase Order Classification Using SVM in POS System at Skus Mart Lestari, Sri; Nadip, Muhamad Zaeni; Akbar, Yuma; Hidayat, Aditya Zakaria; Aula, Raisah Fajri
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.4564

Abstract

In retail business processes, decision-making regarding Purchase Order PO submissions often remains manual and subjective, creating risks that impede procurement efficiency. The study develops an automatic classification model to predict PO approval status using Support Vector Machine SVM algorithm integrated within Point of Sale POS systems. Historical purchase transaction data was obtained from SKUS Mart POS database containing 133 entries, including attributes such as item quantity, purchase price, previous stock levels, and total purchase amounts. The research applies CRISP-DM methodology, encompassing business understanding, data exploration, preprocessing normalization using StandardScaler, model training, evaluation, and deployment phases. The model was trained using linear kernel and validated through holdout technique with 80:20 ratio for training and testing. Test results demonstrate that the SVM model achieves 76.69% accuracy, 82.21% precision, 76.69% recall, and 78.51% F1-score. The model was implemented in a web-based POS system CodeIgniter 3 combined with Python scripts to generate automatic classifications displayed directly in the user interface. Although the model demonstrates adequate performance, the study has not compared its effectiveness against other machine learning algorithms such as Random Forest or K-Nearest Neighbor. These findings establish initial groundwork for machine learning integration to support decision automation in procurement systems.
Optimization of Bandwidth Management and Network Security Using PPPoE Method and Intrusion Detection Prevention System Arham, Muhammad; Akbar, Yuma
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.5021

Abstract

Rural internet networks frequently struggle with unstable connections, bandwidth waste, and cyber vulnerabilities. We optimized bandwidth management and network security by implementing Point to Point Protocol over Ethernet (PPPoE) alongside Intrusion Detection and Prevention System (IDPS). PPPoE manages user authentication and dynamic bandwidth allocation, while IDPS identifies and blocks network threats. Our experimental research took place in Sukanegara Village network environment. Testing involved network load simulations and cyber-attack scenarios to evaluate system performance. We compared network metrics before and after implementation, focusing on bandwidth consumption, threat detection rates, and connection stability. PPPoE implementation reduced bandwidth consumption by 35% through controlled user access and fair distribution mechanisms. The IDPS successfully detected 92% of simulated attack attempts, including port scanning, flooding attacks, and unauthorized access attempts. Network latency dropped significantly during peak usage hours, while connection stability improved across all user categories. The combined PPPoE-IDPS solution effectively addresses rural network challenges. The system delivers cost-efficient bandwidth management while maintaining robust security protection. Implementation requires minimal additional hardware and allows management by local technical staff. Our findings support widespread adoption in community and village-scale networks seeking reliable internet infrastructure with adequate security measures.
Optimization of Data Security Protection with Full SSL Inspection on AWS Using FortiGate Virtual Appliance Akbar, Yuma; Abdillah, Gipari Pradina; Mulyana, Dadang Iskandar; Sutisna, Sutisna
International Journal Software Engineering and Computer Science (IJSECS) Vol. 5 No. 2 (2025): AUGUST 2025
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v5i2.5153

Abstract

The expanding adoption of cloud services, particularly Amazon Web Services (AWS), has intensified challenges in protecting encrypted data traffic within network security frameworks. SSL/TLS protocols, widely utilized for data encryption, have become exploitation vectors for cyber adversaries as conventional security solutions lack the capability to scrutinize encrypted traffic effectively. The research addresses such security gaps by implementing Full SSL Inspection through Fortigate Virtual Appliance deployment within AWS cloud environments. The study examines cloud-based network architecture integrated with Fortigate systems, employing methodologies that encompass virtual appliance installation, SSL/TLS inspection feature configuration, and assessment of system effectiveness alongside performance impact evaluation. Research instruments include simulated cyber-attack scenarios targeting encrypted traffic patterns. Findings demonstrate that Full SSL Inspection significantly enhances threat detection capabilities within network traffic, albeit with measurable increases in system latency and computational overhead. The implementation of Fortigate Virtual Appliance proves effective in strengthening AWS data security postures. Research outcomes emphasize the necessity for configuration optimization to maintain security-performance equilibrium, positioning the solution as viable for organizations prioritizing data protection strategies
Implementation of the Naive Bayes Method in Looker Studio for data on the achievement of Great IDN in IDN Akhwat School Akbar, Yuma; Az-Zahra, Haura Salsabila; Setiawan, Kiki; Fajri, Raisah
Indonesian Journal of Multidisciplinary Science Vol. 3 No. 11 (2024): Indonesian Journal of Multidisciplinary Science
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/ijoms.v3i11.981

Abstract

The IDN Hebat program is an important tool for schools to track and analyze student achievement data. However, with the targeted activities in the IDN program, challenges arise in managing and measuring achievement data efficiently. The research aims to develop a Web Cloud-based data management system for IDN Hisbat achievements at IDN Akhwat School by utilizing Google Looker Studio and the Naive Bayes Algorithm. The data source used in this study is by applying a classification dataset obtained from student achievement information data in the Great IDN Program. The results of this analysis show that the highest accuracy of teaching achievement fell on the status of exceeding the target with a percentage of 89%, and the highest class that placed the status above the target was class 9A with an average percentage of 35%. In addition, the results from this analysis can help coordinators and schools in planning more effective and strategic programs in the future. Overall, this study provides important benefits in improving the quality of teaching and student coaching, as well as supporting data-driven decision-making. This study is expected to enhance the efficiency, accuracy, and effectiveness of managing student achievement, while also supporting the attainment of optimal educational goals for each student to achieve extraordinary results.
Application of the K-Nearest Neighbor Method in Determining Laptop for Classes Qolbi, Rofika; Tundo, Tundo; Putri Wibowo, Salsabila; Akbar, Yuma
International Journal of Law Social Sciences and Management Vol. 1 No. 3 (2024): International Journal of Law Social Sciences and Management
Publisher : Yayasan Meira Visi Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69726/ijlssm.v1i3.31

Abstract

Laptops are one of the basic needs in today's modern life. Laptops are used in a wide variety of activities such as work, study, and entertainment. This research aims to be able to predict the class of laptops in the Ilda Computer store. In this process, the K-Nearest Neighbor Algorithm (KNN) method will be applied. There are 2 types of data that will be used in this study, namely training data totaling 80 data and test data as many as 6 data. In the data, there are 7 criteria that will be used, namely Price, Screen Size, Resolution, OS, RAM, Processor Type, and Laptop Class. In this study, it was obtained that the application of the KNN Algorithm can help in determining the prediction of the Laptop Class. And also the application of the KNN algorithm with K=3 obtained the best performance results with an accuracy value of 50%, a presicion of 50%, and a recall of 66%. Meanwhile, with K=4, the best performance results were obtained with an accuracy value of 50%, presicion of 66%, and recall of 50%. Finally, the K=5 obtained the best performance with an accuracy value of 66%, a presicion of 33%, and a recall of 100%.
Co-Authors AA Sudharmawan, AA Abdillah, Gipari Pradina Abdul Shomad Abdulloh Abror, Ikhsan Adhipramana, Fernanda Aditya Bagas Pramudhi Aditya Zakaria Hidayat Adzani, Adinda Mutiara Agung Pratama Agung Wianata Sugeng Kusuma Agung Wiranata Sugeng Kusuma Ahluna, Faza Ahmad Zulfikar Aidil Rizki Hidayat Aimar, Muqorrobin Akhsani, Ziyat Akmaludin Akmaludin Al Ammaar, Mohammad Farroos Albahy, Abdurrahman Asyam Aldi Sitohang Aldino Nur Ihsan Angga Tristhanaya Anita Rosiana Anwar, Imam Dzikrilloh Arfadhillah, Zahra Arham, Muhammad Ari Ramadhan Arief, Yoga Sofyan Arif, Sulthan Cendikia Arinal, Veri Aula, Raisah Fajri Aulia, Mutia Dwi Awang Hariman, Aloisius Az-zahra, Haura Salsabila Azis, Iim Muhaemin Abdul Aziz Septian Amrullah Azzahra, Yasmin Aulia Bachtiar, Yuliana Bebriani, Serli Benny Sulaiman K Betty Yel, Mesra Bintoro, Bayu Bryan Pratama Cahyono, Bayu Adi Candra Milad Ridha Eislam Dadang Iskandar Mulyana` Dadang Iskandar, Dadang Damayanti, Yulia Dava Septya Arroufu Dedi Dwi Saputra Dewa Gde Adi Murthi Udayana Doddy Mulyadi Saputra Edhy Poerwandono Edhy Poerwandono Eka Satria Maheswara Fadhil Khanifan Achmad Fadillah, Fauzan Fahmi Chairulloh Faisal Akbar Farhani, Aulia Febrianti, Syafira Feni Putriani Fentri Boy Pasaribu Fernanda Adhipramana Gusniar Alfian Noor Hartinah, Suci Sugih Hengky, Mario Hidayat, Aditya Zakaria Ikhwanul Kurnia Rahman Jodi Juliansah Juliansah, Jodi Julianto, Muhammad Rizky Lestari, Dinny Amalia M Ilham Setya Aji M Jundi Hakim Mafazi, Luthfillah Marjuki Maulana Putra Hertaryawan, Ryfan Mayangsari, Descania Meilisa Miftahul Huda Mizsuari Muamar Rizky Muhamad Farisi Muhamad Fikri Nugraha Muhammad Faizal Lazuardi Mulya, Citra Pricylia Ananda Nadip, Muhamad Zaeni Nirat Nirat Nirat, Nirat Novianto, Firza NST, Silvan Nufus, Reda Hayati Nugraha, Muhamad Fikri Nur Arif Khairudin Nur Oktavian Nurfaishal, Muhammad Dzaky Nurmaylina, Vivi Oka Prasetiyo Oky Tria Saputra Oky Tria Saputra7 Permatasari, Veren Nita Piqih Akmal Poerwandono, Edhy Praja Raymond , Samuel Pramudita, Diky Prasetiyo, Oka Putri Wibowo, Salsabila Qibthiyah, Mariyatul Qolbi, Rofika Ramadhan, Muhammad Arya Regita, Anggit Nur Hannaa Rezha Mulia Revandy Richard Franido Richard Franido Rizki Maulana, Rizki Rizky Adawiyah S, Fahmi Chairulloh Widia Safhani, Rizca Sahrul Hidayat Sahrul Hidayat Saputra, Mochammed Erryandra Sarimole, Frencis Matheos Septiansyah, Ade Setiawan, Kiki Sibarani, Julvan Marzuki Putra Sri Lestari SRI LESTARI Sri Lestari Sugiharto, Tri Sugiyono Sugiyono Sugiyono Sumpena Sumpena Sumpena, Sumpena Surapati, Untung Sutisna Sutisna Sutisna Sutisna Suwandi Tegar Muhamad Hafiz Tegar Muhamad Hafiz Toriq, Fatkul Tri Wahyudi Tundo, Tundo Untung Surapati Untung Surapati Untung Wahyudi Untung Wahyudi Wahyu Saputro Wijaya, Rohmat WINDU GATA Yusril Nurhadi AS