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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Informasi dan Ilmu Komputer Scientific Journal of Informatics Journal of Information Technology and Computer Science (JOINTECS) METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Viva Medika: Jurnal Kesehatan, Kebidanan dan Keperawatan JSAI (Journal Scientific and Applied Informatics) Buletin Ilmiah Sarjana Teknik Elektro Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) Jurnal Teknologi Sistem Informasi Jurnal Pengabdian Masyarakat Jurnal Ilmu Komputer dan Teknologi (IKOMTI) SEMINAR NASIONAL PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT Jurnal Pengabdian Pada Masyarakat METHABDI Jurnal Pengabdian dan Pemberdayaan Masyarakat Indonesia
Imam Ahmad Ashari, Imam Ahmad
Universitas Harapan Bangsa
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Implementasi Market Basket Analysis dengan Algoritma Apriori untuk Analisis Pendapatan Usaha Retail Imam Ahmad Ashari; Anggit Wirasto; Deny Nugroho Triwibowo; Purwono Purwono
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 3 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i3.1439

Abstract

Pada era teknologi sekarang hampir semua bisnis ritel sudah menggunakan teknologi Point of Sale (PoS), dimana semua transaksi di rekap dalam sebuah database sistem. Data yang disimpan di dalam database dapat diolah untuk meningkatkan penjualan. Dengan mengetahui asosiasi data penjualan, aplikasi dapat memberikan rekomendasi produk yang memungkinkan pelanggan untuk membeli rekomendasi produk tersebut. Tujuan dari penelitian ini adalah mengetahui pola asosiasi yang terdapat pada sebuah toko yang sudah menerapkan teknologi PoS. Apabila pola asosiasi tersebut membentuk keterhubungan produk yang relevan dan mendatangkan keuntungan lebih maka metode yang di usulkan akan di terapkan pada aplikasi toko. Algoritma Apriori dapat menemukan pola hubungan produk antar satu atau lebih item dalam suatu dataset. Hanya saja Algoritma Apriori memiliki kelemahan dalam performa. Penerapan algoritma apriori dapat memperlambat akses transaksi, sehingga perlu pengkajian lebih dalam tentang kebermanfaatan pola asosiasi ini. Pada penelitian ini pola asosiasi dianalisis apakah berpengaruh terhadap peningkatan penjualan. Dalam penelitian ini didapatkan bahwa pola asosiasi memiliki peran penting dalam peningkatan penjualan. Didapatkan rata - rata asosiasi dengan nilai confidence tertinggi terjadi pada bulan maret, yaitu 0.61 dengan nilai minimal support 0.003. Hal ini sesuai dengan hasil penjualan tertinggi, yaitu sebesar Rp. 295.509.934 pada bulan maret, tahun 2021. Berdasarkan penelitian ini maka penggunaan algoritma apriori pada aplikasi POS perlu diterapkan.
Implementasi dan Evaluasi Kinerja Sistem IoT Multi-Sensor Berbasis ESP32 untuk Pemantauan dan Peringatan Dini Lingkungan secara Real-Time Arif Setia Sandi Ariyanto; Deny Nugroho Triwibowo; Imam Ahmad Ashari; Rito Cipta Sigitta Haryono
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9861

Abstract

Real-time environmental monitoring has become increasingly important due to growing urban and industrial activities that affect air quality, noise levels, and physical environmental stability. However, many existing monitoring systems remain relatively expensive, lack portability, and are limited to passive monitoring functions without clear performance evaluation. This study aims to implement and evaluate the performance of an Internet of Things (IoT)-based multi-sensor environmental monitoring system integrated with a mobile application and real-time early warning features. The system is developed using an ESP32 microcontroller connected to DHT22, MQ135, SW-420, and KY-037 sensors to monitor temperature, humidity, air quality, vibration, and noise levels. Sensor data are transmitted to a server via a RESTful API, stored in a MySQL database, and visualized in real time through a Flutter-based mobile application. The research adopts a Research and Development (R&D) approach, encompassing requirement analysis, system design, implementation, integration, and functional testing. The experimental results indicate that the system can transmit multi-sensor data reliably with low response time, present environmental information in real time, and consistently deliver early warning notifications when environmental parameters exceed the defined threshold values. This study contributes by providing a practical and replicable performance evaluation of an IoT-based multi-sensor system suitable for small-scale environmental monitoring.
Bridging the Gap: Implementasi Aplikasi Portofolio OBE untuk Evaluasi Pembelajaran yang Lebih Efektif dan Efisien Imam Ahmad Ashari; Retno Agus Setiawan; Arif Setia Sandi A.; Deny Nugroho Triwibowo; Anggit Wirasto; Iis Setiawan Mangkunegara; Sony Kartika Wibisono
Jurnal Pengabdian Pada Masyarakat METHABDI Vol 6 No 1 (2026): Jurnal Pengabdian Pada Masyarakat METHABDI
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/methabdi.Vol6No1.pp50-56

Abstract

The implementation of the Outcome-Based Education (OBE) curriculum is often hindered by repetitive manual administration. This community service aims to facilitate digital transformation through SIMPOBE application assistance for 39 lecturers at Universitas Harapan Bangsa. The method involved a hands-on workshop covering assessment alignment, RPS standardization, and PPEPP-based raw score input. Evaluation results show the application successfully bridged the gap between curriculum data and evaluation reports. Efficiency significantly improved as the system automatically links assessment components to CPL/CPMK, eliminating redundant input. Most participants (scores 4 and 5) expressed readiness for independent implementation by April 2026. In conclusion, portfolio digitalization effectively reduces human error and administrative burdens, supporting accountable institutional academic quality enhancement.
Monitoring the pH Levels of Well Water in the Home Industri Sarung Goyor Village, Pemalang, Using IoT Technology and Inverse Distance Weight Method Imam Ahmad Ashari; Purwono Purwono; Irfan Arfianto
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 4 (2023): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i4.27388

Abstract

The Sarung Goyor Home Industry business, located in Wanarejan Utara Village, Pemalang, has been running for several years. However, the use of residual textile dyes in the process of making goyor sarongs now poses a threat to the quality of well water in the area where residents live. This condition is a serious concern because some residents rely on water from the well for drinking, cooking, bathing, and washing. One of the impacts of this textile waste is abnormal water pH. The solution requires real-time monitoring of the pH of well water by utilizing Internet of Things (IoT) technology and pH sensors. In this solution, direct sampling using sensors is carried out at 3 monitoring points around the industrial area and processed to estimate the pH level of residents' well water. This monitoring system succeeded in showing that the average pH of well water was in a safe condition, namely 7.18, not much different from tests carried out with reference sensors, namely a pH range between 6.96 to 7.20. The findings show that in testing the assembled sensor, the IDW method has a measurable error rate with an RMSE of about 0.2629 and a MAPE of about 4.669%. When compared with the test results using a reference sensor, the RMSE value reaches around 0.4666 and the MAPE is around 6.553%.
Enhancement of YOLOv9 Model for Traffic Vehicle Detection using Augmentation Techniques Ashari, Imam Ahmad; Syafei, Wahyul Amien; Wibowo, Adi
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 2 (2026): JUTIF Volume 7, Number 2, April 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.2.5196

Abstract

Traffic vehicle detection is a crucial component in developing intelligent transportation systems, with object detection models like YOLO (You Only Look Once) often preferred for their speed and accuracy. However, challenges remain in detecting vehicles under diverse lighting conditions and small object scales, even with advanced models such as YOLOv9. To address these limitations, image augmentation techniques are employed to enhance model robustness by providing broader data variation. This study investigates the impact of multiple image augmentation methods on the YOLOv9t model for traffic vehicle detection. The techniques evaluated include Blur, Brightness Adjustment, Contrast Adjustment, Color Jitter, Cropping, Flipping, Noise Injection, Rotation, Scaling, and Zoom-In. Results reveal that Scaling and Brightness Adjustment significantly improve detection accuracy, achieving mAP50-95 values of 0.450 and 0.449, respectively. Conversely, methods such as Contrast Adjustment, Rotation, and Cropping produced unsatisfactory outcomes, with Contrast Adjustment performing the worst at only 0.167. Without augmentation, the baseline mAP50-95 was 0.378, emphasizing the vital role of augmentation in improving detection performance, especially under challenging conditions. These findings highlight the importance of selecting appropriate augmentation techniques to optimize YOLOv9t performance, with further improvements possible through combining multiple methods. Compared to approaches that solely focus on enhancing model architecture, the proposed augmentation-based strategy proves more effective in addressing real-world challenges, strengthening resilience against lighting variations and small object detection. This contribution supports the development of more accurate and reliable multilabel vehicle detection systems, advancing safer and more efficient intelligent transportation solutions.
Klasterisasi Pemetaan Kedisiplinan Pegawai Berdasarkan Rekap Kehadiran menggunakan Algoritma Clustering K-Means Imam Ahmad Ashari; Purwono Purwono; Jatmiko Indriyanto; Arif Setia Sandi A.
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 1 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No1.pp12-18

Abstract

Employee discipline is one of the key success factors in a company. Work discipline has an important role in the formation of a positive work environment. One of the things that shows employee discipline is the time of attendance. Attendance time is usually recorded at the time the employee enters and leaves. Disciplinary information can be mapped into several groupings so that it is easy for decision makers to read. One of the computational methods that can perform data mapping is the K-Means Clustering method. The K-Means Clustering method can group data based on their characteristics. In this study, attendance data were analyzed using the K-Means method to obtain disciplinary groupings. The number of Clusters is calculated using the elbow method, 3 Clusters are obtained which are the best Cluster choices, namely Clusters 0, 1, and 2. The data analysis process shows Cluster 2 is the Cluster with the best level of discipline. From the analysis, it shows that the K-Means Clustering method can classify data based on employee discipline. Based on these results, decision makers can be helped in assessing employee discipline at Universita Harapan Bangsa using the disciplinary data grouping that has been made.
Pendekatan Transfer Learning dan SMOTE untuk Klasifikasi Kanker Kulit pada Imbalanced Dataset Lutviana Lutviana; Purwono Purwono; Imam Ahmad Ashari
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No2.pp323-331

Abstract

Skin cancer is one of the most commonly diagnosed cancers worldwide, with the incidence increasing every year. While early detection is a key factor in reducing skin cancer mortality, conventional methods such as biopsy have limitations in terms of cost and invasiveness. This research applies a deep learning based approach for skin cancer classification with Convolutional Neural Networks (CNN) model using transfer learning method. 3 CNN architectures namely MobileNetV2, EfficientNetB0, and DenseNet121 are used to evaluate the performance of the model in detecting skin cancer. One of the main challenges in this research is the imbalanced dataset, which can cause bias in classification. The Synthetic Minority Over-Sampling Technique (SMOTE) was applied to improve the representation of minority classes. The dataset used comes from Kaggle and consists of 2,357 images classified into 9 skin cancer categories. The results show that the transfer learning method combined with SMOTE can significantly improve the accuracy of the model, especially in detecting classes with a smaller number of samples. The evaluation was conducted using accuracy, precision, recall, and f1-score metrics. This research is expected to contribute to the development of an artificial intelligence-based skin cancer detection system that is more accurate, efficient, and can be used as a tool for medical personnel in early diagnosis of skin cancer.
AI Agents for Nursing Task Augmentation: A Focused Literature Overview of Clinical, Operational, Educational, and Governance Implications Sony Kartika Wibisono; Purwono Purwono; Muhammad Ahmad Baballe; Imam Ahmad Ashari; Annastasya Nabila Elsa Wulandari
Viva Medika Vol 19 No 2 (2026)
Publisher : LPPM Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/vm.v19i2.2250

Abstract

The rapid development of artificial intelligence has accelerated the emergence of AI agents, defined as autonomous or semi-autonomous systems that integrate perception, contextual reasoning, decision-making, interaction, and action within defined workflows. Although AI in nursing has been widely reviewed, existing syntheses often combine predictive models, generative tools, decision-support systems, and agent-based architectures, leaving the specific contributions and implementation maturity of AI agents insufficiently differentiated. This focused literature overview examined peer-reviewed publications published between 2021 and 2025 using targeted database searches and a structured narrative synthesis. The review classified the evidence according to agent architecture, automated nursing tasks, implementation maturity, and reported clinical and operational implications. Three overlapping architectural categories were identified: LLM-driven agents, cognitive agents, and multi-agent systems. Applications were concentrated in clinical documentation and handover, predictive monitoring, medication safety, triage, and clinical decision support. The evidence suggests potential improvements in timeliness, documentation consistency, risk detection, workflow integration, and the reduction of repetitive administrative work. The maturity of the evidence varied considerably. Monitoring and sensor-enabled safety systems showed closer links to rsmitheal-world practice, whereas LLM-driven documentation and multi-agent triage systems were more frequently supported by conceptual, prototype, or simulation-based evidence. The synthesis therefore supports supervised task augmentation rather than the replacement of professional nursing judgment. Major implementation requirements include system reliability, bias mitigation, transparent accountability, human oversight, workforce competency, organizational readiness, and clinical governance. Nursing education should prepare practitioners to evaluate AI-generated outputs, recognize uncertainty and inappropriate recommendations, and apply appropriate escalation procedures. Future research should prioritize real-world, multisite, and longitudinal evaluations that measure patient safety, workload redistribution, verification burden, and clinical outcomes. This review provides a focused conceptual distinction between conventional AI decision-support tools and agent-based systems while integrating their clinical, operational, educational, and governance implications.
Co-Authors Adi Wibowo Alamsyah - Alan Bayu Aji Annastasya Nabila Elsa Wulandari Ardianto, Rian Arif Setia Sandi A. Arkananta, Edgina Rangga Azkiyatun Nadroh Deny Nugroho Triwibowo Diani Romiati Farida Istiningrum Handayani, Oktavia Putri Hidayat, Rachman Husna, Aqmal Miftahul Iis Setiawan Mangkunegara Iis Setyawan Mangku Negara Indriyanto, Jatmiko Irfan Arfianto Jatmiko Indriyanto Khoirun Nisa Kiki Alfaini Nurrizki Lutviana Lutviana Mangku Negara, Iis Setiawan Much Aziz Muslim Muhammad Ahmad Baballe Nisa', Khoriun Pradestya Bima Arweina Purwono Purwono Purwono Purwono Purwono, Purwono Purwono, Purwono Raden Bagus Bambang Sumantri Retno Agus Setiawan Rito Cipta Sigitta Haryono S.Pd. M Kes I Ketut Sudiana . Setiawan, Retno Agus Sony Kartika Wibisono Trivilia, Indah Wahyul Amien Syafei Wirasto, Anggit Wulandari, Annastasya Nabila Elsa Yusuf Fadlila Rahman