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FEATURE ALIGNMENT OF THE INTERNAL QUALITY AUDIT SYSTEM BASED ON PPEPP Jollyta, Deny; Hajjah, Alyauma; Mukhsin, Mukhsin; Prihandoko, Prihandoko
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 11, No 3 (2025): Juni 2025
Publisher : Universitas Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i3.3896

Abstract: The Ministry of Education, Culture, Research, and Technology, has developed guidelines for the Internal Quality Assurance System or known as SPMI, that is being implemented through the Internal Quality Audit (IQA) with the PPEPP cycle, namely Determination (P), Implemen-tation (P), Evaluation (E), Control (P), and Improvement (P). Some universities have implemented IQA with system. The problem is that the system does not line well with the PPEPP cycle, which results in unsatisfactory audit results. The purpose of this study is to evaluate how well the university-owned AQI system features in line the PPEPP cycle and to highlight development opportunities. The method used Feature Oriented Domain analysis (FODA) and Acceptance Testing. This study delivered an analysis of IQA system features that consistent with PPEPP. The FODA results were validated by expert and tested with User Acceptance Test (UAT) with 89.98% user response that the system is acceptable. The research contributes to universities' understanding of the features necessary in the AQI system, which has an impact on the perfection of the university AQI system design in accordance with the PPEPP cycle. Keywords: FODA; IQA system; PPEPP cycle; SPMI Abstrak: Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi telah menyusun pedoman Sistem Penjaminan Mutu Internal atau yang dikenal dengan SPMI, yang diimplementasikan melalui Audit Mutu Internal (AMI) dengan siklus PPEPP, yaitu Penetapan (P), Pelaksanaan (P), Evaluasi (E), Pengendalian (P), dan Peningkatan (P). Beberapa perguruan tinggi telah mengimplementasikan AMI dengan sistem. Permasalahannya, sistem tersebut tidak sejalan dengan siklus PPEPP, sehingga hasil audit kurang memuaskan. Tujuan dari penelitian ini adalah untuk mengevaluasi seberapa baik fitur sistem AMI milik perguruan tinggi sejalan dengan siklus PPEPP dan menyoroti peluang pengembangan. Metode yang digunakan adalah analisis Feature Oriented Domain (FODA) dan Acceptance Testing. Penelitian ini menghasilkan analisis fitur sistem AMI yang konsisten dengan PPEPP. Hasil FODA divalidasi oleh ahli dan diuji dengan User Acceptance Test (UAT) dengan 89,98% respon pengguna bahwa sistem dapat diterima. Penelitian ini memberikan kontribusi terhadap pemahaman universitas terhadap fitur-fitur yang diperlukan dalam sistem AMI, yang berdampak pada kesempurnaan desain sistem AMI universitas sesuai dengan siklus PPEPP. Kata kunci: FODA; siklus PPEPP; sistem AMI; SPMI

Comparison of deep learning models: CNN and VGG-16 in identifying pornographic content Chandra, Reza; Suhendra, Adang; Yuniar Banowosari, Lintang; Prihandoko, Prihandoko
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp1884-1899

In 2020, a total of 59,741 websites were blocked by the Indonesian government due to containing negative content, including pornography, with 14,266 websites falling into this category. However, these blocked websites could still be accessed by the public using virtual private networks (VPNs). This prompted the research idea to quickly identify pornographic content. This study aims to develop a system capable of identifying websites suspected of containing pornographic image content, using a deep learning approach with convolutional neural network (CNN) and visual geometry group 16 (VGG-16) model. The two models were then explored comprehensively and holistically to determine which model was most effective in detecting pornographic content quickly. Based on the findings of the comparison between testing the CNN and VGG-16 models, research results showed that the best test results were obtained in the eighth experiment using the CNN model at an epoch value level of 50 and a learning rate of 0.001 of 0.9487 or 94.87%. This can be interpreted that the CNN model is more effective in detecting pornographic content quickly and accurately compared to using the VGG-16 model.

Implementation of YOLOv8 in Object Recognition Systems for Public Area Security in Kebun Raya Bogor Prihandoko, Prihandoko; Rumapea, Sri Agustina; Fawwaz, Muhamad Faishal
ULTIMATICS Vol 17 No 1 (2025): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v17i1.4133

Pedestrian areas often serve as centers of high public activity, requiring intelligent monitoring systems to ensure the safety and comfort of their users. The application of computer vision technology, particularly object detection, offers a promising approach for identifying and estimating the number of individuals in open public spaces. This study implements the YOLOv8 algorithm to develop a human detection and crowd counting model within the pedestrian zones of the Bogor Botanical Garden. Data were collected in the form of images and videos from three strategic locations and annotated using Roboflow with a single object class labeled "person.” The model was trained on the Google Colab platform using a Region of Interest (ROI)-based approach and evaluated through confusion matrix, precision, recall, F1-score, and mean Average Precision (mAP). Results indicate a precision of 0.846, recall of 0.858, F1-score of 0.85, and mAP@50 of 0.951, although a performance drop was observed at mAP@50-95 with a score of 0.586. These findings suggest that YOLOv8 demonstrates strong real-time performance in pedestrian human detection, while challenges remain in enhancing precision under complex and varied conditions.

Transfer Learning Model Evaluation on CNN Algorithm: Indonesian Sign Language System (SIBI) Jollyta, Deny; Prihandoko, Prihandoko; Johan, Johan; Ramdhan, William; Santoso, Erick
Journal of Applied Business and Technology Vol. 6 No. 2 (2025): Journal of Applied Business and Technology
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/jabt.v6i2.213

In Indonesia as much as elsewhere, the deaf can communicate using sign language. The Indonesian Sign Language System (SIBI) is one of the sign language systems used in Indonesia. A model produced by the Convolutional Neural Network (CNN) method can be used in computer science for the recognition of sign language. By using the Transfer Learning paradigm, CNN's performance may be enhanced. However, not many researches have been conducted to assess the effectiveness of transfer learning on sign language models, particularly those that use the TensorFlow library. In fact, the evaluation results can influence the selection of the transfer learning model together with CNN. This study aims to evaluate the efficacy of using the CNN model for SIBI sign language through Transfer Learning. The data used are images of 24 SIBI alphabets and are processed through the TensorFlow library. The images will be recognized through the transfer learning performance of 6 models, namely VGG16, VGG19, Resnet50, Desenet121, Inception-V3 and MobileNet-V2. The results of the study found that through the TensorFlow library, Mobilenetv2 had the highest accuracy of 78% after 20 epochs.

Integrasi Algoritma YOLOv8 dan Streamlit untuk Visualisasi Real-Time dan Akurat dalam Penghitungan Kerumunan di Kawasan Stasiun Bekasi Prihandoko, Prihandoko; Rumapea, Sri Agustina; Pratama, Abdul Hanif
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.pp179-187

Crowd management in public transportation areas has become a critical challenge with the rise of urban populations. This study develops a real-time web-based people detection and counting system by integrating the YOLOv8 algorithm with the Streamlit framework. A case study was conducted at the entrance of Bekasi Station. The model was developed using the AI Project Life Cycle approach, and the system was built following the Waterfall methodology. Data were obtained from video recordings, which were extracted into images, annotated, and processed into training and testing datasets. The YOLOv8 model was trained for 50 epochs, yielding strong performance with an mAP@0.5 of 91.7%, a maximum precision of 93.6%, and an F1-score of 87%. Tests on 15 images showed an average accuracy of 80.37% and an error rate of 19.63%. The model's performance declined on out-of-dataset images due to variations in lighting and extreme crowd density. The system was tested using black-box testing and demonstrated that all main features—image upload, object detection, visualization, and result download—functioned correctly. The system has been successfully deployed on Streamlit Cloud. These results indicate that the system offers a practical, lightweight, and responsive solution to support crowd monitoring in public areas. In future development phases, the system can be extended to support real-time video stream processing and integrated with an object tracking and classification module to accurately identify and differentiate the ingress and egress flow of individuals within a defined surveillance area.

Implementasi Sistem Informasi Manajemen Rantai Pasok pada Rizqy Agung Catering Putra, Anas Sofyan Azhar Surya; Prihandoko, Prihandoko
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol 6, No 04 (2025): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/jrami.v6i04.10519

Rizqy Agung merupakan organisasi bisnis layanan katering yang mengutamakan kepuasan pelanggan. Rizqy Agung dituntut untuk mengirimkan pesanan tepat waktu dengan kualitas makanan yang baik. Namun, staf bagian persediaan mengalami kesulitan dalam manajemen data persediaan bahan makanan dan mengakibatkan tidak terkontrolnya penyimpanan sehingga beberapa diantaranya membusuk. Sistem manajemen persediaan bahan baku makanan yang masih dilakukan secara manual menggunakan kertas mengakibatkan proses berjalan dengan tidak efisien dan akurat. Manajemen rantai pasok merupakan semua aktivitas yang dibutukan dalam mengelola bahan baku untuk menghasilkan produk yang berkualitas bagi pelanggan. Dalam manajemen rantai pasok, terdapat beberapa komponen yang terlibat dalam menciptakan nilai bagi organisasi atau perusahaan. Dengan manajemen rantai pasok, persediaan bahan baku dapat terkelola dengan baik. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem informasi berbasis web dengan menggunakan konsep manajemen rantai pasok yang dapat membantu Rizqy Agung dalam mengelola data persediaan bahan baku makanan. Model pengembangan sistem yang digunakan adalah waterfall. Berdasarkan hasil pengujian penerimaan, sistem layak untuk dioperasikan serta mampu meningkatkan keakuratan dan efiesiensi.

Cluster Validity for Optimizing Classification Model: Davies Bouldin Index â€“ Random Forest Algorithm Prihandoko, Prihandoko; Jollyta, Deny; Gusrianty, Gusrianty; Siddik, Muhammad; Johan, Johan
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4043

Several factors impact pregnant womenâ€™s health and mortality rates. The symptoms of disease in pregnant women are often similar. This makes it difficult to evaluate which factors contribute to a low, medium, or high risk of mortality among pregnant women. The purpose of this research is to generate classification rules for maternal health risk using optimal clusters. The optimal cluster is obtained from the process carried out by the validity cluster. The methods used are K-Means clustering, Davies Bouldin Index (DBI), and the Random Forest algorithm. These methods build optimum clusters from a set of k-tests to produce the best classification. Optimal clusters comprising cluster members withstrong similarities are high-dimensional data. Therefore, the Principal Component Analysis (PCA) technique is required to evaluate attribute value. The result of the research is that the best classification rule was obtained from k-tests = 22 on the 20th cluster, which has an accuracy of 97% to low, mid, and high risk. The novelty lies in using DBI for data that the Random Forest will classify. According to the research findings, the classification rules created through optimal clusters are 9.7% better than without the clustering process. This demonstrates that optimizing the data group has implications for enhancing the classification algorithmâ€™s performance.

Evaluation of The Personnel Information System of The Human Resources Development Agency for Transportation Using the Hot Fit-Model Handayani, Citra; Prihandoko, Prihandoko
Journal of World Science Vol. 2 No. 1 (2023): Journal of World Science
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jws.v2i1.206

In this modern era, it is undeniable that information technology is one of the main resources in an organization that plays an important role in increasing competitiveness and providing optimal service. Every government organization tries to apply information technology to increase service effectiveness and efficiency. This study aims to prove the extent to which the BPSDMP (Human Resources Development Agency for Transportation) staffing information system uses the HOT-FIT Model approach. This study uses a research approach with model evaluation methods. The research involved 62 employees at the Ministry of Transportation's BPSDMP. The data analysis technique used in this study is to use validity test, reliability test, classic assumption test and research hypothesis test. The results show that the BPSDMP Personnel Information System is dominated by human factors with a correlation coefficient value of 0.636, then an organizational correlation coefficient of 0.619 and a technology correlation coefficient of 0.589. In this study, the components of human, organizational and technological factors, together with net benefits, have a coefficient of determination of 56.4%.

Evaluasi Layanan Kesehatan Aplikasi Depok Single Window Dengan Metode System Usability Scale dan Heuristic Evaluation Kusumaningtyas, Amelinda; Prihandoko, Prihandoko
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 1: Februari 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20241117714

Penerapan Smart City di Indonesia merupakan bentuk upaya pemerintah Indonesia dalam pengembangan dan pengelolaan daerah di era desentralisasi. Salah satu contoh implementasi ini adalah e-government di Kota Depok dengan meluncurkan aplikasi mobile bernama Depok Single Window (DSW). Aplikasi DSW merupakan aplikasi layanan publik yang dimiliki oleh Pemerintah Kota Depok untuk meningkatkan kualitas layanan publik memanfaatkan potensi teknologi telematika secara baik. Untuk meningkatkan kesejahteraan rakyat dan kepercayaan masyarakat terhadap keandalan aplikasi layanan publik, perlu diadakan evaluasi aplikasi. Untuk evaluasi ini maka digunakan 2 metode yaitu System Usability Scale (SUS) dan Heuristic Evaluation (HE).Data yang digunakan dalam Metode SUS merupakan data primer yang diperoleh langsung dari masyarakat Kota Depok melalui penyebaran kuesioner dengan Google Form. Data yang digunakan dalam Metode HE juga merupakan data primer yang diperoleh langsung dari para ahli yang memahami usability dari aplikasi mobile dan UI/UX dengan memberikan penilaian berdasarkan 10 prinsip heuristik Nielsen. Dalam Metode SUS didapatkan skor penilaian sebesar 68,75 yang menyatakan bahwa layanan kesehatan yang dievaluasi masih dibawah nilai minimal yang harus didapatkan. Sementara itu, evaluasi yang dilakukan dengan Metode HE menemukan total 13 masalah usability yang telah dievaluasi oleh para ahli. Masalah-masalah tersebut telah melanggar 8 dari 10 prinsip heuristik, yaitu Consistency and Standards, Visibility of System Status, User Control and Freedom, Aesthetic and Minimalist Design, Error Prevention, Recognize, Diagnose, and Recover from Errors, Recognition Rather Than Recall, dan Flexibility and Efficiency of Use.

Integrasi Algoritma YOLOv8 dan Streamlit untuk Visualisasi Real-Time dan Akurat dalam Penghitungan Kerumunan di Kawasan Stasiun Bekasi Prihandoko, Prihandoko; Rumapea, Sri Agustina; Pratama, Abdul Hanif
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.pp179-187

Crowd management in public transportation areas has become a critical challenge with the rise of urban populations. This study develops a real-time web-based people detection and counting system by integrating the YOLOv8 algorithm with the Streamlit framework. A case study was conducted at the entrance of Bekasi Station. The model was developed using the AI Project Life Cycle approach, and the system was built following the Waterfall methodology. Data were obtained from video recordings, which were extracted into images, annotated, and processed into training and testing datasets. The YOLOv8 model was trained for 50 epochs, yielding strong performance with an mAP@0.5 of 91.7%, a maximum precision of 93.6%, and an F1-score of 87%. Tests on 15 images showed an average accuracy of 80.37% and an error rate of 19.63%. The model's performance declined on out-of-dataset images due to variations in lighting and extreme crowd density. The system was tested using black-box testing and demonstrated that all main features—image upload, object detection, visualization, and result download—functioned correctly. The system has been successfully deployed on Streamlit Cloud. These results indicate that the system offers a practical, lightweight, and responsive solution to support crowd monitoring in public areas. In future development phases, the system can be extended to support real-time video stream processing and integrated with an object tracking and classification module to accurately identify and differentiate the ingress and egress flow of individuals within a defined surveillance area.

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