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All Journal International Journal of Evaluation and Research in Education (IJERE) ComEngApp : Computer Engineering and Applications Journal Jurnal Ilmu Komputer dan Informasi Computer Engineering and Applications Journal (ComEngApp) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JUITA : Jurnal Informatika Proceeding of the Electrical Engineering Computer Science and Informatics Computer Engineering and Applications Journal (ComEngApp) Jurnal Informatika Upgris Sinkron : Jurnal dan Penelitian Teknik Informatika JIEET (Journal of Information Engineering and Educational Technology) Jurnal Ilmiah Matrik Indonesian Journal of Information System JITK (Jurnal Ilmu Pengetahuan dan Komputer) JMM (Jurnal Masyarakat Mandiri) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Martabe : Jurnal Pengabdian Kepada Masyarakat Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Jurnal Informatika Global Jurnal Ilmiah Binary STMIK Bina Nusantara Jaya Jurnal Abdimas Mandiri Indonesian Journal of Electrical Engineering and Computer Science Reswara: Jurnal Pengabdian Kepada Masyarakat Journal of Computer Networks, Architecture and High Performance Computing Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Brilliance: Research of Artificial Intelligence Indonesian Community Journal International Journal of Advanced Science Computing and Engineering JEECS (Journal of Electrical Engineering and Computer Sciences) AnoaTIK: Jurnal Teknologi Informasi dan Komputer Jurnal INFOTEL Journal of Computer Science Application and Engineering
Ahmad Sanmorino
Universitas Indo Global Mandiri
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Sosialisasi Aplikasi Augmented Reality MONPERA untuk Pengenalan Pahlawan Nasional dr. AK.Gani Puspasari, Shinta; Haversyalapa, Ditho; Gustriansyah, Rendra; Sanmorino, Ahmad
Jurnal Abdimas Mandiri Vol. 8 No. 2
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jam.v8i2.4088

Abstract

Museum merupakan lembaga yang bertugas menyimpan koleksi benda bernilai sejarah untuk tujuan edukasi maupun rekreasi. MONPERA adalah museum yang memiliki koleksi foto pahlawan terutama berjasa pada perang lima hari lima malamdi Palembang. Koleksi foto disajikan secara tradisional tanpa keterangan yang memberikan informasi bagi pengunjung museum sehingga memerlukan media alternatif untuk mendukung edukasi sejarah pahlawan pada koleksi foto MONPERA. Salah satu pahlawan nasional sekaligus pejuang perang lima hari lima malam di Palembang adalah dr.AK.Gani. Beliau juga memiliki museum yang menyimpan koleksi foto dan benda bernilai sejarah lainnya di Museum dr.AK.Gani. Pengembangan media berbasis teknologi Augmented Reality (AR) foto pahlawan koleksi MONPERA juga dapat dimanfaatkan untuk memperkenalkan sejarah perjuangan dan koleksi foto Musuem dr.AK.Gani. Tujuan kegiatan PkM sosialisasi aplikasi AR foto pahlawan dr.AK.Gani dan koleksi foto lainnya adalah untuk mengenalkan cara pemanfaatan aplikasi yang diharapkan efektif meningkatkan motovasi dan pengetahuan mahasiswa dan pelajar sebagai mayoritas pengunjung museum. Hasil evaluasi kegiatan menunjukkan bahwa aplikasi AR bermanfaat untuk pembelajaran sejarah pahlawan dan memotivasi pengguna untuk belajar sejarah lewat koleksi foto koleksi Museum MONPERA khususnya tentang dr.AK.Gani. Aplikasi AR tersebut diharapkan dapat diperluas dengan penambahan fitur bukan hanya terbatas koleksi foto pahlawan tetapi koleksi benda lainnya di museum sehingga memberikan pengalaman lebih menarik bagi pengunjung museum MONPERA dan dr.AK. Gani serta berdampak pada peningkatan jumlah pengunjung museum.
First Step for Vehicle License Plate Identification Using Machine Learning Approach Amirah; Sanmorino, Ahmad
Journal of Computer Science Application and Engineering (JOSAPEN) Vol. 1 No. 1 (2023): JOSAPEN - January
Publisher : PT. Lentera Ilmu Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70356/josapen.v1i1.6

Abstract

Automated vehicle license plate identification, critical in modern transportation systems, finds application in traffic monitoring, law enforcement, and transportation optimization. This study explores machine learning's potential to enhance accuracy and efficiency in this domain. Leveraging neural networks and pattern recognition, it aims to build an automated system robust across diverse conditions. Addressing limitations in traditional methods, it focuses on adapting to lighting, angles, and image quality variations. The societal impact includes streamlining law enforcement and optimizing traffic flow, revolutionizing transportation and surveillance. Methodologies cover data collection, ethical considerations, preprocessing, feature extraction, model selection, and iterative refinement. Ethical data handling ensures privacy compliance. Feature extraction methods like HOG, LBP, CNNs, and color histograms capture crucial aspects for identification. Model selection spans SVMs, CNNs, decision trees, and ensemble methods, considering task complexity and dataset characteristics. This study evaluates machine learning's potential for revolutionizing license plate identification systems.
The Role of Data Science in Enhancing Web Security Ahmad Sanmorino
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 9 No. 2 (2024): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v9i2.4

Abstract

With the rise of digital transformation, web security has become a critical concern for organizations, governments, and individuals. This study explores the role of data science in enhancing web security by leveraging machine learning algorithms and advanced analytics to predict and identify potential attacks in real-time. The main objective is to demonstrate how data-driven techniques, including predictive analytics, anomaly detection, and behavioral analysis, can be integrated into existing security frameworks to reduce vulnerabilities and strengthen defenses against cyber threats. The research gap addressed by this study lies in the insufficient application of comprehensive, data-driven methodologies for threat detection and classification in web security. The problem gap is the absence of integrated frameworks that combine feature engineering, classification models, and anomaly detection for both known and unknown threats. This study bridges these gaps by employing a structured dataset of web interactions to model, detect, and predict security threats using advanced data science techniques. Using a dataset of simulated web traffic and previous attack records, this research applies data preprocessing, feature engineering, and machine learning classification models, such as decision trees and random forests, to predict threat levels and identify anomalies. Results show that machine learning models can effectively classify threat levels, with a threat classification accuracy of 80 percent. This study contributes to the field by demonstrating how data science can improve web security practices, offering a proactive approach to detecting and mitigating cyber-attacks.
Hukum dan Kebijakan Keamanan Siber: Tantangan Regulasi Perangkat IoT Anwar, Yatama Zahra; Sanmorino, Ahmad
Jurnal Ilmiah Informatika Global Vol. 15 No. 3: Desember 2024
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v15i3.4773

Abstract

The Internet of Things (IoT) has impacted many sectors such as industry, health, and households, with the ability to connect physical objects to the internet network. However, this development is accompanied by major challenges related to cybersecurity, including the risk of data intrusion, cyberattacks, and privacy violations. One fundamental problem is the lack of uniform security standards, which causes various manufacturers' implementation differences. In addition, many IoT devices are not designed with security as a priority, making them vulnerable to attacks. Other challenges include the lack of user awareness of the importance of data security and the limitations of cross-country regulations in monitoring and enforcing IoT security laws. This article explores the challenges in cybersecurity regulation on IoT and offers policies that support increased security. The main contribution of this article is to provide insight into the problems of IoT regulation and provide practical solutions to reduce cyber risks on IoT devices. These solutions are expected to be a guide for policymakers in formulating dynamic regulations, under the development of IoT technology.
ANALISIS TINGKAT KEPUASAN MAHASISWA TERHADAP SISTEM INFORMASI AKADEMIK STEBIS IGM MENGGUNAKAN METODE PIECES FRAMEWORK Kartini, Aprianita; Sanmorino, Ahmad; Terttiavini
AnoaTIK: Jurnal Teknologi Informasi dan Komputer Vol 2 No 1 (2024): Juni 2024
Publisher : Program Studi Ilmu Komputer FMIPA-UHO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33772/anoatik.v2i1.21

Abstract

Academic information systems are one of the information systems most widely used by universities. Academic information systems are designed to meet the needs of universities in providing computerized educational services to improve performance, service quality, competitiveness and the quality of human resources created. The Indo Global Mandiri College of Economics and Sharia Business, known by the abbreviation STEBIS IGM, is a college that has utilized information technology by developing the STEBIS IGM Academic Information System (SIAKAD). The STEBIS IGM Academic Information System is a system that is used as a basis for students' needs to obtain information related to class schedules, filling in KRS, study results cards, student grades, student bills, transcripts of lecture results, consultations, study results and other information. The aim of this research is to determine the level of student satisfaction with the STEBIS IGM academic information system using the Pieces Framework method which consists of six (6) variables, namely Performance, Information and Data, Economic, Control and Security, Efficiency, Service. Respondents in this study were active students in the class of 2021 and class of 2022 who used the STEBIS IGM academic information system. The results that will be obtained from this research are the level of student satisfaction with the STEBIS IGM academic information system. Keywords: Academic Information System, STEBIS IGM, Satisfaction
A Review for the Mechanism of Research Productivity Enhancement in the Higher Education Institution Sanmorino, Ahmad; Karimah, Fitrah
International Journal of Advanced Science Computing and Engineering Vol. 3 No. 1 (2021)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (294.333 KB) | DOI: 10.62527/ijasce.3.1.43

Abstract

The main purpose of this review is to find out the mechanism of research productivity enhancement proposed by each researcher in the papers they have published. The availability of these various mechanisms raises the desire of the authors to compare each mechanism. The focus of the review lies in the mechanism, characteristics, source of data, and evaluation methods used by each researcher. The review then jumps to the results obtained by each mechanism. The author also compares the types of data used by each researcher and the parties involved in the mechanism. There are some differences in the use of terminology between one to another mechanism, but in essence, it has the same goal, research productivity enhancement.
Penyuluhan Aman Berkomunikasi Melalui Whatsapp pada Ponpes di Kelurahan Talang Jambe Palembang Sanmorino, Ahmad; Gustriansyah, Rendra; Puspasari, Shinta
Reswara: Jurnal Pengabdian Kepada Masyarakat Vol 6, No 1 (2025)
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/rjpkm.v6i1.4937

Abstract

WhatsApp menjadi salah satu media komunikasi yang paling banyak digunakan oleh berbagai kalangan, termasuk di lingkungan pondok pesantren, karena kemudahannya dalam berbagi informasi secara cepat dan efisien. Namun, pemahaman tentang keamanan dalam penggunaannya masih sering terabaikan. Di Kelurahan Talang Jambe Palembang, penyuluhan terkait cara berkomunikasi yang aman melalui WhatsApp sangat dibutuhkan untuk melindungi para siswa dan tenaga pengajar dari potensi risiko siber yang dapat mengganggu kegiatan pembelajaran. Pengabdian ini bertujuan untuk meningkatkan kesadaran dan pemahaman para siswa dan tenaga pengajar di Pondok Pesantren Kelurahan Talang Jambe Palembang mengenai pentingnya berkomunikasi secara aman melalui WhatsApp. Diharapkan peserta dapat mengenali potensi risiko siber dan menerapkan langkah-langkah keamanan dalam aktivitas komunikasi sehari-hari. Adapun mitra kegiatan pengabdian ini adalah Pondok Pesantren di Kelurahan Talang Jambe Palembang. Pengabdian dilakukan melalui pendekatan penyuluhan dengan metode ceramah dan diskusi interaktif. Materi yang disampaikan mencakup praktik terbaik dalam penggunaan WhatsApp, seperti pengaturan privasi, pengenalan phishing, dan cara menghindari penipuan daring. Hasil feedback pengabdian ini menunjukkan adanya peningkatan pemahaman para peserta perihal berkomunikasi secara aman melalui WhatsApp. Sebanyak 80 persen peserta menyatakan tidak akan mengklik sembarang tautan pada pesan WhatsApp. Sebanyak 80 persen peserta menyatakan tidak akan memberikan informasi pribadi ke orang tak dikenal. Sebanyak 80 persen peserta menyatakan akan selalu update versi WhatsApp terbaru agar lebih aman.
Tree-based models and hyperparameter optimization for assessing employee performance Gustriansyah, Rendra; Puspasari, Shinta; Sanmorino, Ahmad; Suhandi, Nazori; Sartika, Dewi
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp569-577

Abstract

The Palembang city fire and rescue service (FRS) is encountering challenges in adhering to national standards for fire response time. Hence, the Palembang city FRS is committed to enhancing employee performance through quarterly performance assessments based on various criteria such as attendance, work targets, behavior, education, and performance reports. This study proposes tree-based models in machine learning (ML) and hyperparameter optimization to assess the performance of Palembang city FRS employees. Tree-based models encompass decision trees (DT), random forests (RF), and extreme gradient boosting (XGB). The predictive performance of each model was evaluated using the confusion matrix (CM), the area under the receiver operating characteristic (AUROC), and the kappa coefficient (KC). The results indicate that RF performs better than DT and XGB in the sensitivity, AUROC, and KC metrics by 1.0000, 0.9874, and 0.8584, respectively.
Improving Information Security with Machine Learning Sanmorino, Ahmad; Gustriansyah, Rendra; Puspasari, Shinta; Alie, Juhaini
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3317

Abstract

The study Improving Information Security with Machine Learning explores the fusion of machine learning methodologies within information security, aiming to fortify conventional protocols against evolving cyber threats. By conducting a comprehensive literature review and empirical analysis, this scholarly endeavor highlights the efficacy of machine learning in anomaly detection, threat identification, and predictive analytics within security frameworks. Through practical demonstrations, such as z-score-based anomaly detection in network traffic data and NLP-based email security systems, the study illustrates the practical applications of machine learning techniques. Additionally, it delves into the mathematical underpinnings of predictive analytics and the architecture of neural networks for malware detection. However, while showcasing the transformative potential of machine learning, the study also confronts significant challenges. Ethical, legal, and privacy considerations emerge prominently, emphasizing the need for regulations addressing algorithmic biases, ethical dilemmas, and data protection. Moreover, the study emphasizes the practical challenges of scalability, interpretability, continual adaptation to evolving threats, and the harmonious interaction between human expertise and machine intelligence. By offering practical recommendations and future research directions, this scholarly exploration aims to empower researchers, practitioners, and policymakers in navigating the complex intersection of machine learning and information security, thereby fostering innovation and comprehension in this evolving domain.
Toddlers’ Nutritional Status Prediction Using the Multinomial Logistics Regression Method Gustriansyah, Rendra; Suhandi, Nazori; Puspasari, Shinta; Sanmorino, Ahmad; Sartika, Dewi
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3372

Abstract

Malnutrition is one of the foremost health problems experienced by children under five in many countries, especially in low and middle-income countries. Meanwhile, the target of Sustainable Development Goals (SDGs) 2.2 is that all forms of malnutrition must end by 2025. Therefore, this study aims to predict the toddlers’ nutritional status (malnutrition, undernutrition, overnutrition, and normal nutrition) based on age, body mass index (BMI), weight, and length using the Multinomial Logistic Regression (MLR) classification method. The dataset consists of two hundred toddlers obtained from the Kaggle site. Following pre-processing, the dataset is divided, with 80 percent of the data for training and the remaining 20 percent for testing. The model was trained using 10-fold cross-validation (CV). In Addition, the MLR model performance was evaluated using the confusion matrix (CM), the area under the curve (AUC), and the Kappa coefficient (KC). The evaluation results using CM show that the accuracy, sensitivity, and specificity values are 0.9412, 0.9375, and 0.9790, respectively. AUC and KC also show excellent results. It indicates that the MLR method is an esteemed and recommended method for predicting the nutritional status of toddlers. Therefore, this research can contribute to providing early information so that the Government can immediately determine the necessary treatment.
Co-Authors Ade Fathurahman Agustina Heryati Aldri Frinaldi Amalia, Rianti Amirah Antony, Fery Anwar, Yatama Zahra Ariati, Nining Aulia, Beta DEWI SARTIKA Dhamayanti Dhamayanti Dhamayanti Dhamayanti Dhamayanti Dhamayanti, Dhamayanti Ditho Hersilava Dwi Asa Verano Ermatita - Faradillah Faradillah Faradillah Fery Antony Hartini Hartini Haversyalapa, Ditho Heryati, Agustina Imelda Saluza, Imelda Isabella Isabella Isabella, Isabella Juhaini Alie Juliansyah, Muhammad Agung Karimah, Fitrah Kartini, Aprianita Kesuma, Hendra Di Marcellina, Dona Marnisah, Luis Masroni Dedi Kiswanto Maulana, Muhammad Mico Megat Norulazmi Megat Mohamed Noor Muhammad Farhan Nazori Suhandi Purbasari, Nadira Putri Rendra Gustriansyah Ristina, Andan Rudi Heriansyah Sartika, Dewi Setiadi Yazid, Setiadi Setiawan, Ichan Shinta Puspasari Shinta Puspasari Suharmanto, Arvia Fildzah Sunardi, Hastha Suryati Suryati Sutrisna, Rangga Terttiaavini Terttiaavini, Terttiaavini Terttiaavini, Terttiaavini Terttiavini Verano, Dwi Asa Wiyanto, Ari Wulanda, Sanza Vittria Yovita, Sukma Yulianti, Evi