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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Dinamik JURNAL BISNIS STRATEGI Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Scientific Journal of Informatics JAIS (Journal of Applied Intelligent System) ANDHARUPA Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Analisis Bisnis Ekonomi EBSJ JURIKOM (Jurnal Riset Komputer) KOMPUTIKA - Jurnal Sistem Komputer EDUMATIC: Jurnal Pendidikan Informatika Abdimasku : Jurnal Pengabdian Masyarakat Indo-MathEdu Intellectuals Journal JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Rekayasa elektrika Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Analisis Bisnis Ekonomi
Muljono Muljono
Department of Pediatrics, Faculty of Medicine, Universitas Indonesia/ Cipto Mangunkusumo Hospital
Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences

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Penyusunan Analisis Kebutuhan Perangkat Lunak untuk Web Profil SMP Negeri 7 Semarang Utomo, Danang Wahyu; Kurniawan, Defri; Zeniarja, Junta; Dewi, Ika Novita; Salam, Abu; Muljono, Muljono
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 8, No 1 (2025): JANUARI 2025
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v8i1.2700

Abstract

Penggunaan web profil sebagai alat penyebaran informasi telah banyak digunakan pada institusi Pendidikan utamanya sekolah. SMP N 7 Semarang menggunakan web profil untuk menyampaikan informasi terkait identitas sekolah seperti visi dan misi sekolah, kurikulum serta kegiatan siswa dalam sekolah. Namun web tersebut masih terdapat kekurangan dan perlu diperbaiki menyesuaikan dengan perkembangan saat ini. Pemahaman tentang analisis kebutuhan perangkat lunak penting bagi para guru dan tenaga pendidik untuk mengetahui kebutuhan pengguna dan kebutuhan sistem yang harus disediakan dalam sistem. Program pengabdian Masyarakat dilaksanakan dalam bentuk pelatihan kepada para guru dan tenaga pendidik. Para peserta diberikan materi analisis kebutuhan termasuk kebutuhan pengguna, kebutuhan sistem, kebutuhan fungsional dan non-fungsional. Selain itu, para peserta juga menerima pelatihan tentang desain antarmuka pengguna dan tata letak konten situs web. Hasil dari program ini, para peserta dapat mengidentifikasi perbaikan yang diperlukan untuk situs web profil SMP N 7 Semarang. Fitur berita diidentifikasi sebagai kebutuhan fungsional yang perlu ditambahkan pada situs web profil. Untuk kebutuhan non-fungsional, para peserta menyarankan desain ulang tata letak konten web
Hakikat Manusia (Peserta Didik) Sebagai Makhluk Pedagogik Kartini, Kartini; Muljono, Muljono; Yuspiani, Yuspiani
Indo-MathEdu Intellectuals Journal Vol. 6 No. 2 (2025): Indo-MathEdu Intellectuals Journal (In-Press)
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/imeij.v6i2.2592

Abstract

This research aims to find out the nature of human beings (students) as pedagogic beings. This article uses the literature study method to explore the concept of human beings (students) as pedagogical beings. The literature review process begins with the identification of relevant sources, including journal articles, books, conference papers, and peer-reviewed scientific articles that discuss the essence of humanity and its relationship with education. Data analysis techniques using critical analysis are used to evaluate various theoretical frameworks that discuss the relationship between humanity and education. The results of the analysis show that humans as pedagogic creatures are humans who have the potential to be educated and educated, and the nature of humans (learners) as pedagogic creatures, based on nature that has the basic potential of hearing, vision, and heart as instruments in the educational process. Humans are creatures that can be educated and educated (homo educandum). The educational process that makes human beings the subject and object of education. Students as pedagogical creatures will describe that they have various characteristics and potentials that need to be understood in the context of education. This understanding is essential to creating a learning environment that supports their psychic and spiritual development optimally
Hakikat Manusia (Peserta Didik) Sebagai Makhluk Pedagogik Kartini, Kartini; Muljono, Muljono; Yuspiani, Yuspiani
Indo-MathEdu Intellectuals Journal Vol. 6 No. 2 (2025): Indo-MathEdu Intellectuals Journal
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/imeij.v6i2.2592

Abstract

This research aims to find out the nature of human beings (students) as pedagogic beings. This article uses the literature study method to explore the concept of human beings (students) as pedagogical beings. The literature review process begins with the identification of relevant sources, including journal articles, books, conference papers, and peer-reviewed scientific articles that discuss the essence of humanity and its relationship with education. Data analysis techniques using critical analysis are used to evaluate various theoretical frameworks that discuss the relationship between humanity and education. The results of the analysis show that humans as pedagogic creatures are humans who have the potential to be educated and educated, and the nature of humans (learners) as pedagogic creatures, based on nature that has the basic potential of hearing, vision, and heart as instruments in the educational process. Humans are creatures that can be educated and educated (homo educandum). The educational process that makes human beings the subject and object of education. Students as pedagogical creatures will describe that they have various characteristics and potentials that need to be understood in the context of education. This understanding is essential to creating a learning environment that supports their psychic and spiritual development optimally
Peningkatan Kinerja K-Means dengan Normalisasi Min-Max dan PSO untuk Penentuan Pusat Awal Klaster Terbaik pada Data Pamsimas nikus, Domi; Muljono, Muljono; Himawan, Heribertus
Dinamik Vol 30 No 2 (2025)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v30i2.10221

Abstract

K- means termasuk kedalam algoritma partisi yang memiliki tujuan untuk membagi data kedalam jumlah klaster yang ditentukan, hasil dari algoritma K means tergantung pada pemilihan pusat klater awal namun permasalahan yang sering terjadi jika pada pemilihan centroid awal yang masih dilakukan secara acak jauh dari solusi maka kemungkinan hasil dari pengelompokan tersebut kurang tepat. Untuk mengatasi masalah tersebut akan menggunakan tahapan preprocessing berupa normalisasi minmax untuk mengatasi skala pengukuran berbeda pada dataset, setelah itu menggunakan algoritma PSO dalam pemilihan centroid awal untuk algoritma K- means, dalam penelitian ini juga dibandingkan pemilihan centroid dengan 3 cara yang pertama sesuai dengan acak, kedua sesuai standar pemerintah untuk nilai kualitas air minum yang tingi, menengah dan rendah kemudian yang ketiga dengan metode yang diusulkan algortima PSO dan kemudian akan diuji dengan Index Davies Bouldin. Hasil pengujian berupa tersebut metode K-means dengan pemilihan centroid awal acak dengan nilai 0,208856082, metode K-means dengan pemilihan centroid sesuai dengan standar pemerintah tentang kondisi SAM sebesar 0,280077, dan terakhir metode pemilihan yang terbaik adalah dengan menggunakan normalisasi minmax K-means PSO dengan nilai 0,177796. Sehingga pengujian data PAMSIMAS menggunakan normalisasi minmax k-means PSO yang didapat bahwa metode tersebut lebih optimal
Pelatihan Penggunaan Aplikasi Screen Reader JAWS Bagi Tunanetra Untuk Meningkatkan Kemampuan Dalam Pengelolaan Administrasi Paramita, Cinantya; Sudibyo, Usman; Muljono, Muljono; Supriyanto, Catur
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 2, No 2 (2019): Juli 2019
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (586.68 KB) | DOI: 10.33633/ja.v2i2.46

Abstract

Salah satu permasalahan yang dihadapi Perkumpulan Penyandang Disabilitas Indonesia (PPDI), Dewan Pengurus Cabang (DPC) Kota Semarang yakni terbatasnya media dan prasarana untuk mendukung kegiatan belajar mereka dalam mengoperasikan komputer serta tingkat perekonomian yang hanya cukup untuk memenuhi kebutuhan hidup, meraka pun belum sepenuhnya paham dalam perkembangan teknologi hingga sampai saat ini semua masih diolah dalam bentuk manual yakni melaporkan dengan lisan dan dengan cara mengingat. Untuk mengatasi permasalahan tersebut, pengabdian ini mengusulkan untuk mengadakan pelatihan penggunaan Job Access with Speech (JAWS) bagi para tuna netra. Pengabdian dilakukan dengan mengajarkan penggunaan dasar keyboard yang didukung oleh aplikasi JAWS dan pengetahuan dasar Microsoft Excel.
Enhancing challenge-based immersion in cultural game using appreciative fuzzy logic Muljono, Muljono; Haryanto, Hanny; Andono, Pulung Nurtantio; Nugroho, Raden Arief; Yakub, Fitri; Sukmono, Indriyo K.
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3702-3714

Abstract

Many traditional games in Indonesia are considered cultural heritage and are in serious decline; young generations no longer know about them. Serious games have been considered a potential educational tool for cultural heritage preservation. Lack of immersive experience due to over-focus on the learning content is a common problem in those games. Very little research also discusses cultural heritage serious game design frameworks. This study uses the appreciative fuzzy logic system (AFLS) to enhance the challenge-based immersive experience (CBIE) in the Joglosemar cultural heritage game. The AFLS provides autonomous challenges, such as enemy numbers and aggressive behavior, and the frequency of item appearances in the games using fuzzy logic with respect to the appreciative serious games (ASG) concepts. The ASG is the design guide for serious games that divides the game activities into 4-D: discovery, dream, design, and destiny. We use three ASG-based serious games to evaluate the CBIE produced by AFLS. The game experience questionnaire (GEQ) is used to measure the player experience, while the cross-validation is used to measure the AFLS performance. Results show that the AFLS enhances the CBIE. The study contributes mainly to provide reliable intelligent system for automated serious game design.
Improving the Accuracy of House Price Prediction using Catboost Regression with Random Search Hyperparameter Tuning: A Comparative Analysis Hartono, Faezal; Muljono, Muljono; Fanani, Ahmad
Advance Sustainable Science Engineering and Technology Vol. 6 No. 3 (2024): May - July
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i3.602

Abstract

Achieving a significant improvement over traditional models, this study presents a novel approach to house price prediction through the integration of Catboost Regression and Random Search Hyperparameter Tuning. By applying these advanced machine learning techniques to the King County Dataset, we conducted a thorough regression analysis and predictive modeling that resulted in a marked increase in accuracy. The baseline model, a conventional linear regression, provided a foundation for comparison, evaluating performance metrics such as R-squared and Mean Squared Error (MSE). The meticulous hyperparameter tuning of the Catboost model yielded a remarkable improvement in predictive accuracy, demonstrating the efficacy of sophisticated data science techniques in real estate and property valuation. The percentage increase in accuracy over the baseline model is explicitly stated in the abstract.
Comparative Analysis of Homogeneous and Heterogeneous Ensembles for Diabetes Classification Optimization Maulana, Muhammad Naufal; Muljono, Muljono; Meindiawan, Eka Putra Agus
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14439

Abstract

Diabetes mellitus is a chronic disease with an increasing prevalence worldwide, including in Indonesia, reaching 11.7% by 2023. Early prediction of this disease is essential for more effective management. This study aims to develop a diabetes mellitus prediction model using an ensemble learning approach, including homogeneous (boosting and bagging) and heterogeneous (stacking and blending) techniques. In this study, the boosting algorithm using AdaBoost with Random Forest as the base estimator showed the highest accuracy of 98%, with balanced precision and recall. The bagging technique, which also uses Random Forest as the base estimator, achieved 97% accuracy, although slightly lower than boosting. The stacking technique, which combines XGBoost, Gradient Boosting, and Random Forest as base learners, with Random Forest as the meta-model, yields similar accuracy of 98%, but with lower prediction error, demonstrating its ability to cope with more complex data. Blending, which uses a similar approach but with training on the entire dataset, gave 98% accuracy with shorter processing time and more efficient memory usage than stacking.
Gaussian Based-SMOTE Method for Handling Imbalanced Small Datasets Misdram, Muhammad; Noersasongko, Edi; Purwanto, Purwanto; Muljono, Muljono; Pamuji, Fandi Yulian
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.v9i4.26881

Abstract

The problem of dataset imbalance needs special handling, because it often creates obstacles to the classification process. A very important problem in classification is to overcome a decrease in classification performance. There have been many published researches on the topic of overcoming dataset imbalances, but the results are still unsatisfactory. This is proven by the results of the average accuracy increase which is still not significant. There are several common methods that can be used to deal with dataset imbalances. For example, oversampling, undersampling, Synthetic Minority Oversampling Technique (SMOTE), Borderline-SMOTE, Adasyn, Cluster-SMOTE methods. These methods in testing the results of the classification accuracy average are still relatively low. In this research the selected dataset is a medical dataset which is classified as a small dataset of less than 200 records. The proposed method is Gaussian Based-SMOTE which is expected to work in a normal distribution and can determine excess samples for minority classes. The Gaussian Based-SMOTE method is a contribution of this research and can produce better accuracy than the previous research. The way the Gaussian Based-SMOTE method works is to start by determining the random location of synthesis candidates, determining the Gaussian distribution. The results of these two methods are substituted to produce perfect synthetic values. Generated synthetic values are combined with SMOTE sampling of the majority data from the training data, produce balanced data. The result of the balanced data classification trial from the influence of the Gaussian Based SMOTE result in a significant increase in accuracy values of 3% on average.
Analysis Kernel and Feature: Impact on Classification Performance on Speech Emotion Using Machine Learning Gondohanindijo, Jutono; Noersasongko, Edi; Pujiono, Pujiono; Muljono, Muljono
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29022

Abstract

The main objective of this study is to test the machine learning kernel's selection against the characteristics of the data set used, resulting in good classification performance. The goal of speech emotion recognition is to improve computers' ability to detect and process human emotions in order to improve their ability to respond to interactions between people and computers. It can be applied to feedback on talks, including sentimental or emotional content, as well as the detection of human mental health. One field of data mining work is Speech Emotion Recognition. One of the important things in data mining research is to determine the selection of the kernel Classifier, know the characteristics of datasets, perform Engineering Features and combine features and Corpus Datasets to obtain high accuracy. The research uses analysis and comparison methods using private and public datasets to detect speech emotions. Experimental analysis was done on the characteristics of datasets, selection of kernel classifiers, pre-processing, feature and corpus datasets fusion. Understanding the selection of a classifier kernel that matches the characteristics of the dataset, engineering features and the merger of features and datasets are the contributions of this investigation to improving the accuracy of the classification of speech emotion data. For models with the selection of kernels that match the characteristics of their datasets, this study gave an increase in accuracy of 12.30% for the private dataset and 14.80% for the public dataset, with accuracies of 100.00% and 74.80% respectively. Combining features and public datasets provides an increase in accuracy of 33.62% with an accuracy of 73.95%.
Co-Authors Abu Salam Achmad Wahid Kurniawan Ahmad Fanani Alya Salsabila, Anggita Arisani, Muhammad Irfan Astuti, Rahma Hayuning Bayu Aryanto Budi Harjo Budi, Setyo Catur Supriyanto Edi Noersasongko Fandi Yulian Pamuji Fitri Yakub Hanny Haryanto Hartono, Faezal Harun Al Azies Hendarwanto Hendarwanto Herdiman T. Pohan Heribertus Himawan Herowati, Wise Ignatius Moses Setiadi, De Rosal Ika Novita Dewi Junta Zeniarja Jutono Gondohanindijo, Jutono Karmel L. Tambunan Kartini Kartini Kurniawan, Defri Kusumo, Willyanto Kartiko Mahazam Afrad Maulana, Muhammad Naufal Meindiawan, Eka Putra Agus Misdram, Muhammad Moch Anjas Aprihartha Muhammad Naufal, Muhammad Muliadi, Nuraziz nikus, Domi Nugroho, Bagus Nur Rokhman Paramita, Cinantya Prasetyo Prasetyo Pujiono Pujiono Pulung Nurtantio Andono Purwanto Purwanto R. H.H. Nelwan Rachmad Abdullah Raden Arief Nugroho Sari Ayu Wulandari Setyaningrum, Rani Setyaningrum, Rani Silalahi, Rosalia Natal Sudibyo, Usman Sukmono, Indriyo K. Supriadi Rustad Susanto, Al Birr Karim Sutriawan Sutriawan T. Sutojo Taufiq M. Waly Trisnapradika, Gustina Alfa Utomo, Danang Wahyu Valentina Widya Suryaningtyas, Valentina Widya Wuryantoro, Tri Yuspiani, Yuspiani Zahrah, Febrina Nabila