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All Journal International Journal of Electrical and Computer Engineering Majalah Forum Teknik UGM TEKNIK JURNAL SISTEM INFORMASI BISNIS Jurnal Sistem Komputer Jurnal Sains dan Teknologi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika Jurnal Teknologi dan Sistem Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Penelitian Pendidikan IPA (JPPIPA) Syntax Literate: Jurnal Ilmiah Indonesia Jurnal Pengembangan Rekayasa dan Teknologi Jurnal Sisfokom (Sistem Informasi dan Komputer) Journal of Information Systems and Informatics Jurnal Ilmu Teknik dan Komputer Jurnal Nasional Teknik Elektro dan Teknologi Informasi Buletin Abdi Masyarakat (BAM) Prosiding Seminar Nasional Unimus Scientific Journal of Informatics
Oky Dwi Nurhayati
Departemen Teknik Komputer, Fakultas Teknik, Universitas Diponegoro
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Other

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BUSINESS INTELLIGENCE MODEL OF REGIONAL HOSPITALS USING HGOD DISCOVERY Hengki, Hengki; Gernowo, Rahmat; Nurhayati, Oky Dwi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Based on data from the Regional General Hospital in the Bangka Belitung Islands province, the Gross Death Rate (GDR) is the general death rate for every 1000 patients discharged of 108,430 compared to the health department standard of <45. The Net Death Rate (NDR) is the death rate 48 hours after being treated for every 1000 patients discharged of 67,388 compared to the health department standard of <25. TOI (Turn Over Interval) is the average turnover period of days where a bed is unoccupied from being filled to the next time it is filled of 19,832 days compared to the health department standard of 1 to 3 days. The solution offered by the researcher develops Business Intelligence (BI) optimization with a new model called the HGO (Hierarchy, Governance, Outlook) Discovery approach as a framework model for developing business intelligence for regional general hospitals in Indonesia. This model is expected to be able to solve or reduce the dimensional problems that exist in hospitals, namely the main patient management, HR Key Resources, and the quality of inpatient health services. The HGO Discovery approach is able to find patterns in a series of events called sequences by sorting the work patterns that exist in the hospital so that the business process of regional general hospitals is faster and more interactive in decision making. The Business Intelligence approach carried out by regional hospitals with HGOD is expected to make patient health services more integrated through the hierarchy of patient services, governance and outlook in decision making. 
Pneumothorax Detection System in Thoracic Radiography Images Using CNN Method Fardana, Nouvel Izza; Isnanto, R. Rizal; Nurhayati, Oky Dwi
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.16635

Abstract

Purpose: This research aims to develop an automatic pneumothorax detection system using Convolutional Neural Networks (CNN) to classify thoracic radiography images. By leveraging CNN's effectiveness in identifying medical abnormalities, the system seeks to enhance diagnostic accuracy, reduce evaluation time, and minimize subjective interpretation errors. The output will provide a predicted label of "pneumothorax" or "non-pneumothorax," facilitating faster clinical treatment and improving diagnostic services while supporting radiologists in making more accurate and efficient decisions for this critical condition. Methods: This research employs an experimental deep learning approach using Convolutional Neural Networks (CNN) to detect pneumothorax in thoracic radiography images. The CNN model is trained on an annotated dataset with preprocessing steps, including zooming, brightness adjustment, flipping and format adjustment, followed by performance evaluation using accuracy, precision, recall, and F1 score metrics. Result: The results showed that the CNN model detected pneumothorax with 79.59% accuracy, a loss of 1.3056, and 1,092 correct predictions out of 1,372 test data. Precision was 51.12%, recall 78.62%, and F1 score 61.96%, confirming the system's potential, though further optimization is needed. Novelty: The novelty of this research lies in developing an automated pneumothorax detection system using a CNN architecture, improving diagnostic accuracy and efficiency. Despite high accuracy, precision and recall can be improved. Future research can focus on optimizing the model and applying data augmentation techniques.
Development of Enterprise Architecture in Improving the Efficiency of Hajj and Umrah Services of the Ministry of Religious Affairs of Lubuklinggau City with TOGAF Rosyada, Amrina; Mustafid, Mustafid; Nurhayati, Oky Dwi
Jurnal Sistem Informasi Bisnis Vol 15, No 1 (2025): Volume 15 Number 1 Year 2025
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/vol15iss1pp126-133

Abstract

The Ministry of Religious Affairs of Lubuklinggau City organizes government affairs in Hajj and Umrah services. This research aims to design an enterprise architecture that can align the implementation of information systems with ongoing business activities, to improve the quality of hajj and umrah services at the Ministry of Religious Affairs of Lubuklinggau City. The method used in this study is the TOGAF Architecture Development Method (ADM), which consists of several phases: introduction, architectural vision, business architecture, data architecture, application architecture, technology architecture, and opportunities and solutions. The results of this study are in the form of an enterprise architecture blueprint that includes artifacts in the form of diagrams, catalogs, and matrices to describe existing conditions and proposed target conditions. In addition, this study produces a roadmap as a reference for implementing the architectural design that has been made. In conclusion, designing an enterprise architecture using TOGAF ADM can support the integration of information systems and business activities, thus potentially increasing the efficiency and effectiveness of hajj and umrah services at the Ministry of Religious Affairs of Lubuklinggau City.
User Satisfaction Analysis of SUS Method of Virtual Tours Website for Semarang Chinatown Tourism Experience Muhammad Reza Setiawan; Dinar Mutiara Kusumo Nugraheni; Oky Dwi Nurhayati
Jurnal Penelitian Pendidikan IPA Vol 11 No 1 (2025): January
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i1.9891

Abstract

This article analyzes the satisfaction of website-based virtual tour users using the usability scale system method for tourism experiences in Chinatown Semarang for respondents who use the virtual tour application both in the initial and final applications. This research aims to compare user satisfaction by analyzing the score calculation results from 80 respondents from each virtual tour application using the system usability scale method where the virtual tour in the initial application and the virtual tour in the final application have differences in the addition of a simple interactive website and a guide in the form of a manual book and video tutorial in the final application. The system usability scale d calculation score was obtained with an increase in value of 7.875. This proves that modifying the application and adding a guide feature in the form of a manual book and video tutorial can help respondents use a virtual tour in Semarang's Chinatown village. The advice obtained in this research is that it is hoped that for future research, you can use paid hosting because, in the study here, the virtual tour application still uses free hosting, which still has limitations in processing files, so it takes longer for users to access the virtual tour in Semarang Chinatown Village.
Business Intelligence Model of Regional Hospitals using HGOD Discovery Hengki, Hengki; Gernowo, Rahmat; Nurhayati, Oky Dwi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i1.2320

Abstract

Based on data from the Regional General Hospital in the Bangka Belitung Islands province, the Gross Death Rate (GDR) is the general death rate for every 1000 patients discharged of 108,430 compared to the health department standard of <45. The Net Death Rate (NDR) is the death rate 48 hours after being treated for every 1000 patients discharged of 67,388 compared to the health department standard of <25. TOI (Turn Over Interval) is the average turnover period of days where a bed is unoccupied from being filled to the next time it is filled of 19,832 days compared to the health department standard of 1 to 3 days. The solution offered by the researcher develops Business Intelligence (BI) optimization with a new model called the HGO (Hierarchy, Governance, Outlook) Discovery approach as a framework model for developing business intelligence for regional general hospitals in Indonesia. This model is expected to be able to solve or reduce the dimensional problems that exist in hospitals, namely the main patient management, HR Key Resources, and the quality of inpatient health services. The HGO Discovery approach is able to find patterns in a series of events called sequences by sorting the work patterns that exist in the hospital so that the business process of regional general hospitals is faster and more interactive in decision making. The Business Intelligence approach carried out by regional hospitals with HGOD is expected to make patient health services more integrated through the hierarchy of patient services, governance and outlook in decision making.
SISTEM PAKAR MENDETEKSI GANGGUAN OBSESSIVE COMPULSIVE DISORDER MENGGUNAKAN METODE BACKWARD CHAINING Ikhsan, Hammas Zulfikar; Nurhayati, Oky Dwi; Windarto, Yudi Eko
Jurnal Transformatika Vol. 17 No. 1 (2019): July 2019
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v17i1.1276

Abstract

Obsessive Compulsive Disorder (OCD) adalah salah satu jenis gangguan psikologi yang berasal dari rasa cemas dan takut yang muncul secara tiba-tiba dan tidak dapat dikendalikan oleh penderitanya. Jika gangguan ini dibiarkan maka akan mengganggu aktivitas sehari-hari penderita dan menyebabkan depresi. Dalam proses diagnosa oleh dokter, pasien menggunakan kata yang tidak pasti seperti jarang , lumayan, dan cukup dalam menjawab pertanyaan dari dokter. Hal ini menyebabkan dokter kesulitan dalam melakukan diagnosa. Dari permasalahan diatas, maka dibuatlah aplikasi sistem pakar mendeteksi gangguan obsessive compulsive disorder menggunakan metode backward chaining dan certainty factor untuk memudahkan dan meningkatkan tingkat kepercayaan dokter dalam mendeteksi gangguan obsessive compulsive disorder pasien. Aplikasi ini menggunakan ilmu kecerdasan buatan yaitu metode backward chaining dan certainty factor yang digunakan dalam perancangan dan pembuatan aplikasi sistem pakar. Aplikasi yang dibuat berbasis website menggunakan bahasa PHP dan MySQL. Dari penelitian ini, dihasilkan sistem pakar yang dapat menampilkan kemungkinan dideritanya tipe gangguan OCD. Hasil pengujian aplikasi telah sesuai dengan pengetahuan dari pakar. Pengujian sistem aplikasi menggunakan pengujian black box yang menunjukan semua fungsi yang ada pada aplikasi dapat berjalan sesuai yang diharapkan.  
SISTEM PENDUKUNG KEPUTUSAN PENENTUAN LAHAN KRITIS MENGGUNAKAN PREFERENCE RANKING ORGANIZATION METHOD FOR ENRICHMENT EVALUATION (PROMETHEE) Windarto, Yudi Eko; Fathuddin, Harits; Nurhayati, Oky Dwi
Jurnal Pengembangan Rekayasa dan Teknologi Vol. 3 No. 2 (2019): November (2019)
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/jprt.v15i2.1644

Abstract

Critical land becomes a specific problem in data processing in the environmental field. Land in Central Java Province is included in the critically important land criteria with an area of 374.000 hectares. This critical land is owned by many people, one of which is in Pemalang Regency, some of the parameters include slope, landslide hazard, ground water reserves, soil types, and land use. Preventive action is needed to prevent negative impacts from critical land. Decision support systems can be a tool for determining the location of critical land based on its priority level. Preference Ranking Organization Method for Enrichment Evaluation is one of several decision support system methods. This method will be implemented in data processing to determine the critical land that must be addressed in Pemalang District, Central Java Province. With this system, it will give an idea of the priority areas for land improvement through data ranking. This system was built using PHP programming language and MySQL database. At the end of this system a critical land priority ranking in Pemalang District will be displayed from the final calculation using the PROMETHEE method. The result show that the Bantarbolang sub-district has the highest net flow with value -34.10 as the region with the highest critical land priority.
Global Research Trends and Map on Machine Learning Applications in Stunting Detection in Vulnerable Populations: A Bibliometric Analysis Bachri, Otong Saeful; Widodo, Catur Edi; Nurhayati, Oky Dwi
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1248

Abstract

Stunting and malnutrition continue to be significant public health challenges, particularly in low-income and rural populations. With the growing reliance on data-driven strategies in public health, machine learning (ML) has emerged as a promising tool for identifying, classifying, and predicting conditions related to undernutrition. This study presents a bibliometric analysis of global research from 2019 to 2025, focusing on the application of ML techniques—such as clustering, support vector machines (SVM), and random forest—in addressing malnutrition and stunting. A total of 417 Scopus-indexed publications were analyzed using Biblioshiny (R) to assess research trends, key themes, influential authors, prominent journals, and thematic evolution. The analysis reveals a consistent growth rate of 10.72% in publications, with notable contributions from China and other low- and middle-income countries. Keyword mapping highlights that “machine learning,” “spatial analysis,” and “stunting” are central to the research, although they remain areas for further development. Thematic evolution indicates a shift towards more integrated, context-aware approaches, with a growing focus on built environments and vulnerable populations. The study concludes that while ML holds significant promise for advancing decision-making in child health and nutrition, its impact will depend on continued methodological refinement and effective implementation within public health systems.
Model Prediksi Kinerja Siswa Berdasarkan Data Log LMS Menggunakan Ensemble Machine Learning Ardianti, Mifta; Nurhayati, Oky Dwi; Warsito, Budi
JST (Jurnal Sains dan Teknologi) Vol. 12 No. 3 (2023): Oktober
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jstundiksha.v12i3.59816

Abstract

Institusi pendidikan saat ini menerapkan Learning Management System (LMS) sebagai sarana pembelajaran online. LMS dapat merekam sejumlah besar data perilaku siswa pada log LMS. Data perilaku ini dapat dikumpulkan dan digunakan untuk memprediksi kinerj belajar siswa. Sehingga, diperlukan analisis yang dapat mengubah sejumlah data yang tersimpan tersebut menjadi sebuah pengetahuan yang dapat meningkatkan kualitas pengajaran pada institusi pendidikan. Pada penelitian ini, mengusulkan model prediksi kinerja belajar siswa menggunakan ensemble machine learning berdasarkan ekstraksi ciri yang berhubungan dengan interaksi siswa pada LMS. Pemodelan dilakukan dengan menerapkan tiga jenis ensemble machine learning yaitu ; bagging, boosting dan voting. Hasil penelitian menunjukkan bahwa model ensemble machine learning yaitu bagging, boosting dan voting berhasil digunakan untuk memprediksi kinerja siswa dengan accuracy sebesar 81.25% dengan percision 0.810, recall 0.812 dan f-measure 0.809 yang diperoleh model bagging. Temuan pada penelitian ini adalah ensemble machine learning dapat diterapkan sebagai model prediks kinerja siswa berdasarkan data Log LMS. Institusi pendidikan baik sekolah maupun perguruan tinggi diharapkan dapat merancang sebuah kurikulum LMS untuk meningkatkan kualitas akademik institusi tersebut. Selain itu institusi pendidikan dapat memprediksi bagaimana kinerja siswanya, sehingga dapat meningkatkan prestasi akademik.
Co-Authors Achmad Hidayatno Adhi Susanto Adi Mora Tunggul Adi, Yudi Restu Agung Budi Prasetijo Agung Budi Prasetijo Agus Subhan Akbar, Agus Subhan Agus Subkhi Hermawan Agus Supriyanto Ahmad Aviv Mahmudi Ahmad Muzami Aji Yudha Alim Muadzani Ambrina Kundyanirum Amrina Rosyada Anggi Anugraha Putra Anggit Sri Herlambang Anggoro Mukti Anisa Eka Utami Annisa Hedlina Hendraputri Arief Puji Eka Prasetya Atik Zilziana Muflihati Noor Aulia Medisina Ramadhan Bayu Surarso Budi Warsito Catur Edi Widodo Damar Wicaksono Danal Meizantaka Daeanza Dania Eridani Dania Eridani Dania Eridani Deryan Gelrandy Diana Nur Afifah, Diana Nur Dinar Mutiara Kusumo Nugraheni Dwiana Okviandini Eggy Listya Sutigno Eko Didik Widianto Eko Sediyono Fardana, Nouvel Izza Fathuddin, Harits Febi Andrea Renatha Galuh Boy Hertantyo Gayuh Nurul Huda Hadi Hilmawan Hanna Mariana Baun, Hanna Mariana hastuti, Isti Pudji Hendra Pria Utama Hengki Hengki Ike Pertiwi Ike Pertiwi Windasari Ike Pertiwi Windasari Ikhsan, Hammas Zulfikar Imaduddin Abdul Rahim Indra Aditia Indra Permana Isti Pudjihastuti Julce Adiana Sidette, Julce Adiana Juwanda, Farikhin Keszya Wabang Kurniawan Teguh Martono Kusworo Adi Lazuardi Arsy Lia Dorothy M Irfan Syarif Hidayatullah M. Rizki Kurniawan Maesadji Tjokronagoro Menur Wahyu Pangestika, Menur Wahyu Mey Fenny Wati Simanjuntak Mifta Ardianti Migunani Migunani Muhammad Hafiz Tsalavin Muhammad Nasrullah Muhammad Naufal Prasetyo Muhammad Reza Setiawan Muhammad Ridwan Asad Mustafid Mustafid Naretha Kawadha Pasemah Gumay Ningrum, Alifvia Arvi Ninik Rustanti Nofiyati Nofiyati, Nofiyati Nugraheni, Dinar Nugroho Adhi Santoso Nurazizah Nurazizah Nurhuda Maulana Nurul Arifa Nuryanto . Otong Saeful Bachri Prio Pambudi R Rizal Isnanto R Rizal Isnanto R. Rizal Isnanto R. Rizal Isnanto Rahmat Gernowo Reza Najib Hidayat Rian Haris Muda Nasution Rinta Kridalukmana Risma Septiana Rismawan Fajril Falah Riyadhi Sholikhin Satriaji Cahyo Nugroho Siswo Sumardiono Sri Widodo, Thomas Suryo Mulyawan Raharjo Suryono Suryono Teguh Hananto Widodo Thomas Sri Widodo Tristy Meinawati Tyas Panorama Nan Cerah Ulinuha, Ajik Wahyul Amien Syafei Wijaya Wahyudi Akbar Yessy Kurniasari Yudhi Kasih Pasaribu Yudi Eko Windarto Yudi Restu Adi Yusraka Dimas Al Iman Yusuf Arya Yudanto Zaskia Wiedya Sahardevi