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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 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 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 Medicine & Pharmacology Nursing Public Health Social Sciences Other

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K-NN Based Air Classification as Indicator of the Index of Air Quality in Palembang Sanmorino, Ahmad; Alie, Juhaini; Ariati, Nining; Wulanda, Sanza Vittria
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 3 (2022): Article Research Volume 6 Number 3, July 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i3.11469

Abstract

Good air quality is something that is wanted by every human who lives in big cities. Clean air and no pollution is one of the proper environmental requirements. One of the most severe causes of air pollution is due to large-scale forest fires due to the long dry season or is carried out by irresponsible persons which they commonly refer to as land clearing in an easy and inexpensive way by utilizing the reason of the dry season. The purpose of this study is to classify air quality in Palembang using a data mining approach. Then use the results of the classification as an indicator of the level of air quality in the city of Palembang. The data mining approach that researchers use is the K-Nearest Neighbor algorithm. Based on the test results of K-NN calculations and measured using a confusion matrix produce an accuracy of 80 percent, 82.3 percent for precision, and 93.3 percent for recall. The measurement results show that the calculation using the K-NN algorithm can be used as an indicator in measuring air quality, of the 20 that have been trained and tested only 4 inaccurate data, this inaccuracy occurs because the source data has unbalanced classes such as unhealthy and very unhealthy healthy have 1 sample each. So it proves that the performance of classifiers using the K-NN algorithm relevant as an indicator of air quality levels in the city of Palembang.
Fine-tuning a pre-trained ResNet50 model to detect distributed denial of service attack Sanmorino, Ahmad; Kesuma, Hendra Di
Bulletin of Electrical Engineering and Informatics Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i2.7014

Abstract

Distributed denial-of-service (DDoS) attacks pose a significant risk to the dependability and consistency of network services. The utilization of deep learning (DL) models has displayed encouraging outcomes in the identification of DDoS attacks. Nevertheless, crafting a precise DL model necessitates an extensive volume of labeled data and substantial computational capabilities. Within this piece, we introduce a technique to enhance a pre-trained DL model for the identification of DDoS attacks. Our strategy’s efficacy is showcased on an openly accessible dataset, revealing that the fine-tuned model we propose surpasses both the initial pre-trained model and other cutting-edge approaches in performance. The suggested fine-tuned model attained 95.1% accuracy, surpassing the initial pre-trained model as well as other leading-edge techniques. Please note that the specific evaluation metrics and their values may vary depending on the implementation, hyperparameter settings, number of datasets, or dataset characteristics. The proposed approach has several advantages, including reducing the amount of labeled data required and accelerating the training process. Initiating with a pre-existing ResNet50 model can also enhance the eventual model’s accuracy, given that the pre-trained model has already acquired the ability to extract significant features from unprocessed data.
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.
Co-Authors Ade Fathurahman Agustina Heryati Aldri Frinaldi Amalia, Rianti Amirah Amirah 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 Nining Ariati 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 Terttiaavini Terttiaavini, Terttiaavini Terttiaavini, Terttiaavini Terttiavini Verano, Dwi Asa Wiyanto, Ari Wulanda, Sanza Vittria Yovita, Sukma Yulianti, Evi