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All Journal Register: Jurnal Ilmiah Teknologi Sistem Informasi Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Journal of Innovation and Future Technology (IFTECH) Jurnal Teknik Informatika C.I.T. Medicom Jurnal Sistem Informasi Indonesian Journal Computer Science (ijcs) International Journal of Enterprise Modelling
Pujiastuti, Lise
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Transportation

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Rancangan Sistem Informasi Akademik (SIAKAD) Program Pascasarjana Universitas Negeri Jakarta (UNJ) Pujiastuti, Lise
Jurnal Sistem Informasi Vol 7 No 2 (2018): Vol VII No.2 Agustus 2018
Publisher : STMIK ANTAR BANGSA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (345.92 KB)

Abstract

Abstract— Universities are one of the agents of change that undertake the task of developing human resource tasks through formal education process. Graduate Program Jakarta State University is a leading educational institution in Jakarta. But there are still less information systems that are still manual, as in academic management is generally more of a written method such as student input value, documentation absent, assignment to students. Most of the students already know and utilize information technology in communication tools, but in reality educational institutions do not have information systems that are not in line with the development of students' knowledge of the technology they use. With these shortcomings, the researchers provide solutions that will be useful for the Graduate Program of State University of Jakarta for its development, namely by website-based academic information system using PHP programming language. With this website will facilitate the institution in providing accurate and fast information for all users both students, lecturers and the general public. Intisari - Perguruan tinggi merupakan salah satu agen perubahan yang mengemban tugas mengembangkan tugas sumber daya manusia melalui proses pendidikan formal. Program Pascasarjana Universitas Negeri Jakarta merupakan lembaga pendidikan yang terkemuka di Jakarta. Tetapi masih ada yang kurang yaitu sistem informasi yang masih manual, seperti dalam pengelolaan akademik pada umumnya lebih bersifat metode tertulis misalnya input nilai mahasiswa, pendokumentasian absen, pemberian tugas kepada mahasiswa. Sebagian besar mahasiswa sudah mengenal dan memanfaatkan teknologi informasi pada alat komunikasinya, tetapi pada kenyataannya institusi pendidikan belum memiliki sistem informasi yang tidak sejalan dengan perkembangan pengetahuan mahasiswa akan teknologi yang mereka pakai. Dengan kekurangan ini maka peneliti memberikan solusi yang akan berguna bagi Program Pascasarjana Universitas Negeri Jakarta untuk perkembangannya, yaitu dengan sistem infomasi akademik berbasis website dengan menggunakan bahasa pemrograman PHP. Dengan adanya website ini akan memudahkan pihak institusi dalam memberikan informasi yang akurat dan cepat untuk semua user baik mahasiswa, dosen dan masyarakat umumnya Kata Kunci: Informasi, Pemrograman, Website
Enhancing Multiplication Skills: The Way Modeling Method and Mathchess Games in Educational Practice Pujiastuti, Lise; Wahyudi, Mochamad
International Journal of Enterprise Modelling Vol. 17 No. 3 (2023): September: Enterprise Modelling
Publisher : International Enterprise Integration Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/emod.v17i3.78

Abstract

This research delves into the exploration of an innovative educational approach aiming to enhance multiplication skills among students. The study investigates the combined efficacy of the Way Modeling Method, utilizing visual representations, and Mathchess games, a gamified learning approach, in improving multiplication proficiency. Through a quasi-experimental design involving a control and experimental group, elementary school students aged 8 to 10 were exposed to either traditional instruction or the combined intervention. Pre-tests and post-tests were administered to measure changes in multiplication skills, accompanied by qualitative assessments through participant feedback and observations. The results unveiled significant improvements in the experimental group, indicating a substantial enhancement in accuracy, comprehension, engagement, and confidence in solving multiplication problems. Comparative analysis between groups highlighted the distinct effectiveness of the combined methodology, aligning with cognitive learning theories and emphasizing the potential for dynamic and interactive pedagogical approaches in fostering mathematical skills. These findings present implications for educational practice, advocating for the integration of diverse teaching methodologies catering to varied learning styles. Furthermore, they pave the way for future research in optimizing these approaches and exploring their broader applications in mathematical education.
Accurate and Objective Lecturer Appraisal System: Implementation of the LOPCOW Method Sumanto, Sumanto; Radiyah, Ummu; Supriyatna, Adi; Pujiastuti, Lise; Yani, Ahmad; Marita, Lita Sari
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol 21, No 2 (2024): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Ilmu Komputer, FMIPA, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v21i2.10188

Abstract

This research proposes the use of the Logarithmic Precursor Chain-Driven Objective Weighting (LOPCOW) method to evaluate the best lecturers in universities.  The LOPCOW method ensures that the assessment covers all aspects of lecturer quality and performance, including education, research, community services, discipline, commitment, cooperation skills, and innovation. The evaluation of lecturers using the scores and ratings provided showed that CDE lecturers were the best, with the highest score of 0.715.  CDE lecturers showed high consistency in all aspects assessed, especially in education, research, and community service.  This was followed by MNO lecturers (0.676), STU lecturers (0.668), XYZ lecturers (0.637), and AFI lecturers (0.627). In conclusion, highly ranked lecturers showed strong dedication to the Tridharma of higher education, with consistent performance and a positive impact on the academic community and the general public.  Future research should focus on developing strategies to improve lecturers' teaching quality by applying new educational technologies and evaluating their impact on student learning. 
PEMILIHAN DOSEN TELADAN BERPRESTASI DENGAN METODE MULTI ATTRIBUTE UTILITY THEORY (MAUT) Pujiastuti, Lise; Amin, Ruhul; Hariyanto, Hariyanto; Supriyatna, Adi; Christian, Ade; Sumanto, Sumanto
Journal of Innovation And Future Technology (IFTECH) Vol 6 No 2 (2024): Vol 6 No 2 (August 2024): Journal of Innovation and Future Technology (IFTECH)
Publisher : LPPM Unbaja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/iftech.v6i2.3398

Abstract

This study aims to evaluate the performance of lecturers in higher education using the Multi Attribute Utility Theory (MAUT) method. The main problem faced is the complexity of assessing lecturers based on the Tri Dharma of Higher Education-education, research, and community service-as well as the challenges of subjectivity and inefficiency in manual assessment. MAUT was chosen due to its ability to consider various assessment criteria in a structured and objective manner and follows the standardization of outstanding lecturer assessment including: Education, Research, Community Service, Discipline, Commitment, Cooperation Ability and Ability to innovate. The results showed that Adi Fajar Insani had the best performance with a total final score of 1.01, while Dian Eka Fitriani had the lowest score of 0.00. The MAUT method proved effective in providing a comprehensive and fair assessment, overcoming the limitations of traditional methods that are not thorough. The conclusion of this study is that the application of MAUT can improve the objectivity, efficiency, and accuracy of the lecturer evaluation process, thus encouraging the improvement of lecturer quality and productivity in various fields. Further research is recommended to develop more relevant assessment criteria, involve larger samples, and explore the use of more sophisticated technology to support the assessment process.
Pengembangan Sistem Deteksi Objek Botol Real-Time dengan YOLOv8 untuk Aplikasi Vision Triyanto, Dedi; Zidan, Muhammad; Wahyudi, Mochamad; Pujiastuti, Lise; Sumanto, Sumanto
Indonesian Journal Computer Science Vol. 3 No. 1 (2024): April 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijcs.v3i1.6070

Abstract

Plastik daur ulang berperan penting dalam menanggulangi masalah limbah lingkungan sekaligus mendukung praktik keberlanjutan. Penelitian ini bertujuan mengembangkan sistem deteksi botol plastik dan kaleng daur ulang secara real-time menggunakan algoritma YOLOv8 yang terkenal akan kecepatan dan akurasinya. Dengan memanfaatkan dataset yang terdiri dari 2.900 gambar dan melatih model melalui Google Colab selama 25 epoch, penelitian ini berhasil menunjukkan performa luar biasa dari YOLOv8, dengan hasil mAP sebesar 99,5%, precision 99,7%, dan recall 99,5%. Model ini terbukti sangat efektif dalam mendeteksi objek daur ulang, memberikan prediksi yang tepat tanpa kesalahan negatif pada confusion matrix. Untuk penelitian lanjutan, disarankan menambah variasi kelas objek seperti botol kaca dan karet serta memperluas dataset guna meningkatkan generalisasi model. Selain itu, pengujian dalam kondisi nyata sangat diperlukan untuk memastikan kinerja optimal dalam lingkungan yang lebih kompleks. Pendekatan serupa dalam penelitian sebelumnya juga telah membuktikan kinerja unggul dalam deteksi real-time, menjadikan metode ini salah satu yang terdepan dalam pengembangan teknologi berbasis YOLO.
Deteksi dan Prediksi Cerdas Penyakit Paru-Paru dengan Algoritma Random Fores Kurniawan, Deny; Wahyudi, Mochamad; Pujiastuti, Lise; Sumanto, Sumanto
Indonesian Journal Computer Science Vol. 3 No. 1 (2024): April 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijcs.v3i1.6071

Abstract

Penyakit paru-paru, seperti COPD, kanker paru-paru, dan asma, adalah masalah kesehatan global yang menyebabkan lebih dari tujuh juta kematian setiap tahun. Teknologi canggih, termasuk model deep learning dan algoritma Random Forest, telah digunakan secara efektif untuk mendeteksi dan mengklasifikasikan penyakit paru-paru dari data pencitraan dengan akurasi tinggi. Penelitian ini bertujuan menunjukkan efektivitas algoritma Random Forest dalam memprediksi penyakit paru-paru. Dataset yang digunakan terdiri dari 30.000 data dengan 11 atribut, diperoleh dari Kaggle dan diproses menggunakan perangkat lunak Orange versi 3.36.2. Algoritma Random Forest diimplementasikan dengan 10 pohon keputusan dan enam atribut yang dipertimbangkan pada setiap pembagian data. Model ini diuji menggunakan validasi silang dengan 10 lipatan, dan hasil pengujian menunjukkan nilai AUC sebesar 0,993, yang mengindikasikan tingkat akurasi yang sangat tinggi. Matriks kebingungan digunakan untuk mengevaluasi kinerja model, dengan mengukur akurasi, presisi, recall, F1-Score, dan AUC. Model ini menunjukkan akurasi yang tinggi, dengan nilai ROC AUC 0,453 untuk prediksi adanya penyakit paru-paru dan 0,547 untuk prediksi ketiadaan penyakit paru-paru. Hasil ini menunjukkan bahwa algoritma Random Forest dapat menjadi alat yang efektif dalam mengidentifikasi penyakit paru-paru. Penelitian ini berkontribusi pada pengembangan teknik diagnostik yang lebih akurat dan efisien, yang dapat membantu tenaga medis dalam mendiagnosis penyakit paru-paru pada pasien. Dengan pemahaman yang lebih baik tentang penerapan algoritma ini dalam dunia kesehatan, diharapkan dapat meningkatkan kualitas diagnosis dan perawatan pasien secara signifikan.
Advanced graph neural networks for dynamic yield optimization and resource allocation in industrial systems Pujiastuti, Lise; Wahyudi, Mochamad
Jurnal Teknik Informatika C.I.T Medicom Vol 16 No 2 (2024): May: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/cit.Vol16.2024.785.pp90-102

Abstract

This research explores the integration of Graph Neural Networks (GNNs) and Reinforcement Learning (RL) for dynamic yield optimization and resource allocation in industrial systems. We present a numerical example involving a small manufacturing setup with three machines, where GNNs are employed to model complex interactions and derive meaningful embeddings of machine states. These embeddings are then used to predict yield and cost through linear combination functions. RL is utilized to optimize resource allocation dynamically, balancing yield and cost through a carefully designed reward function. The results demonstrate the effectiveness of GNNs in capturing machine interactions and the adaptability of RL in optimizing operational parameters in real-time. This combined approach showcases significant potential for enhancing efficiency, cost-effectiveness, and overall performance in various industrial applications, providing a robust framework for continuous improvement and adaptive decision-making in dynamic environments.
Comparison of Manhattan and Chebyshev Distance Metrics in Quantum-Based K-Medoids Clustering Solikhun, Solikhun; Siregar, Muhammad Rahmansyah; Pujiastuti, Lise; Wahyudi, Mochamad; Kurniawan, Deny
Sistemasi: Jurnal Sistem Informasi Vol 14, No 4 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i4.5193

Abstract

Anemia is a condition characterized by a decrease in the number of red blood cells or hemoglobin levels in the bloodstream. It can lead to fatigue and reduced productivity. Clustering is a technique in data mining used to identify patterns that can support decision-making processes. In the case of anemia, clustering plays a crucial role in identifying various severity patterns and understanding the contributing factors behind the condition. Quantum computers, which utilize the principles of quantum mechanics for information processing, have made significant advancements over the past decade. Quantum computing is an advanced method of information processing that leverages qubits, enabling systems to exist in multiple states simultaneously. This technology offers the potential to solve complex problems at exponentially faster speeds than classical computers. In this study, researchers applied the K-Medoids clustering algorithm, calculated using quantum-based equations. The research compares two distance measurement methods: Chebyshev distance and Manhattan distance. The results show that the Manhattan algorithm performs better in medical contexts, particularly for detecting positive cases, with a recall of 0.57 and an F1-score of 0.695, although it has a slightly lower precision of 0.88. This makes it more suitable for medical applications where false negatives carry high risks, such as disease detection, despite its higher cost and mean squared error (MSE). On the other hand, Chebyshev distance achieved perfect precision (1.0) and higher accuracy (80%), but its low recall (0.33) indicates that many positive cases were missed. Therefore, Manhattan distance is more recommended for medical applications that require the detection of more positive cases, while Chebyshev is more efficient for scenarios that prioritize accuracy and cost.
Manhattan Distance-based K-Medoids Clustering Improvement for Diagnosing Diabetic Disease Solikhun; Rahmansyah Siregar, Muhammad; Pujiastuti, Lise; Wahyudi, Mochamad
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 6 (2024): December 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i6.5894

Abstract

Diabetes is a metabolic disorder characterized by blood glucose levels above normal limits. Diabetes occurs when the body is unable to produce sufficient insulin to regulate blood sugar levels. As a result, blood sugar management becomes impaired and there is no cure for diabetes. Early detection of diabetes provides an opportunity to delay or prevent its progression into acute stages. Clustering can help identify patterns and groups of diabetes symptoms by analyzing attributes that indicate these symptoms. In this study, researchers are using K-Medoid and Quantum K-Medoid methods for clustering diabetes data. Quantum computing utilizes quantum bits, or qubits, which can represent multiple states at the same time. Compared to classical computers, quantum computing has the potential for an exponential speedup in problem-solving. Researchers conducted a comparison between two methods: the classic K-Medoids method and the K-Medoids method utilizing quantum computing. The researchers found that both Quantum K-Medoid and Classic K-Medoid achieved the same clustering accuracy of 91%. In testing with the Quantum K-Medoids algorithm, it was found that the cost value in the 8th epoch showed a significant decrease compared to the Classical K-Medoids algorithm. This demonstrates that Quantum K-Medoid can be considered a viable alternative for clustering purposes.
Enhancing Lung Cancer Prediction Accuracy UsingQuantum-Enhanced K-Medoids with Manhattan Distance Solikhun, Solikhun; Pujiastuti, Lise; Wahyudi, Mochamad
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i3.4190

Abstract

Lung cancer is a leading cause of cancer-related deaths worldwide, and early detection plays a crucialrole in improving treatment outcomes. This study proposes an enhancement of the K-Medoids clusteringmethod by integrating a quantum computing approach using Manhattan distance to improveprediction accuracy for lung cancer diagnosis. The research was conducted using a publicly availablelung cancer dataset consisting of 309 patient records with 14 diagnostic attributes. Comparative experimentswere carried out between the classical K-Medoids and the quantum-enhanced K-Medoids, withperformance evaluated based on clustering accuracy, precision, recall, and F1-score. The results showthat the quantum-based method has the same accuracy as the classical method, namely 88%. Thissuggests that quantum-based clustering can match the accuracy of classical methods after adequatetraining, although consistency and parameter stability remain areas for further refinement. Furtherresearch is recommended to test the model on larger datasets and to explore real-world deployment inclinical decision support systems.
Co-Authors Ade Christian, Ade Adi Supriyatna ahmad yani Dedi Triyanto Deni Kurniawan, Deni DENY KURNIAWAN Firmansyah Firmansyah HARIYANTO HARIYANTO Hengki Tamando Sihotang Herman Mawengkang Lita Sari Marita Mochamad Wahyudi Rahmansyah Siregar, Muhammad Ruhul Amin, Ruhul Siregar, Muhammad Rahmansyah Solikhun Solikhun Solikhun, Solikhun Sumanto Sumanto Sumanto, Sumanto Ummu Radiyah, Ummu Weber, Gerhard-Wilhelm Zidan, Muhammad