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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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Homonym and polysemy approaches with morphology extraction in weighting terms for Indonesian to English machine translation Harjo, Budi; Muljono, Muljono; Abdullah, Rachmad
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp7036-7045

Abstract

Homonym and polysemy features can influence some errors in translation from a source language to another target language, for example, from Indonesian to English. A lemma or a morphology factor can cause the configuration of Indonesian homonym features. For example, the word beruang can mean an animal beruang (bear) and can mean a verb alternation ber+uang (has/have money). The Indonesian polysemy feature can also impact an error in the translation process because it can have a literal meaning and a symbolic meaning. For example, the terms bunga melati (jasmine flower) and bunga hati (lover), where bunga does not only mean flower. Therefore, the development machine translation (MT) method needs to capture homonym and polysemy features in the form of a word or a phrase. This research proposes homonym and polysemy approaches with morphology extraction in weighting terms for Indonesian to English MT. First, this research uses morphology extraction to detect sentences that contain prefixes, lemma, and suffixes. Then, the word similarity measurement functions to extract homonym and polysemy in the form of uni-gram and bi-gram using bidirectional encoder representations from transformers (BERT) embedding, named entity recognition (NER), synonym-based term expansion, and semantic similarity. This research uses neural machine translation for the translation process.
Deep learning for audio signal-based tempo classification scenarios Muljono, Muljono; Nurtantio Andono, Pulung; Ayu Wulandari, Sari; Al Azies, Harun; Naufal, Muhammad
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1687-1701

Abstract

This article explains how to determine the tempo of the kendhang, an Indonesian traditional melodic instrument. This research presents novelty as technological research related to gamelan instruments, which has rarely been achieved thus far, through the introduction of kendhang tempo types through the sounds produced, with the hope of creating an automatic system that can recognize the kendhang tempo during a gamelan performance. The testing in this work will categorize the tempo of kendhang into three categories: slow, medium, and fast, utilizing one of the two scenario models proposed, mel frequency cepstral coefficients (MFCC) and convolutional neural network (CNN) in the first scenario, and mel spectrogram and CNN in the second. Kendhang's original audio data, which was captured in real time and later enhanced, makes up the data set. The model 1 scenario, which entails feature extraction using MFCC and classification using the CNN classification approach, is the best scenario in this research, based on the experimental results. When compared to the other suggested modeling scenarios, model 1 has a level of 97%, an average accuracy, and a gain value of 96.67%, making it a solid assistant in terms of kendhang's good tempo recognition accuracy.
Comparison of String Similarity Algorithm in post-processing OCR Susanto, Al Birr Karim; Muliadi, Nuraziz; Nugroho, Bagus; Muljono, Muljono
Journal of Applied Intelligent System Vol. 8 No. 1 (2023): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i1.7079

Abstract

The Optical Character Recognition (OCR) problem that often occurs is that the image used, has a lot of noise covering letters in a word partially. This can cause misspellings in the process of word recognition or detection in the image. After the OCR process, we must do some post-processing for correcting the word. The words will be corrected using a string similarity algorithm. So what is the best algorithm? We conducted a comparison algorithm including the Levenshtein distance, Hamming distance, Jaro-Winkler, and Sørensen – Dice coefficient. After testing, the most effective algorithm is the Sørensen-Dice coefficient with a value of 0.88 for the value of precision, recall, and F1 score
Multivariate Analysis Model in Measuring the Level of the Efficiency Islamic Banking Industry Kusumo, Willyanto Kartiko; Muljono, Muljono
Economics and Business Solutions Journal Vol. 3 No. 2 (2019): Economics and Business Solutions Journal
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (720.458 KB) | DOI: 10.26623/ebsj.v3i2.1577

Abstract

Efficiency is one of the performance parameters underlying an organization. Efficiency in the world of banking is one of the performance parameters that is quite popular and widely used because it is the answer to the difficulties in calculating banking performance measurements. The measurement of banking efficiency can be done with Multivariate Analysis. Multiveriate is a methodology for determining relative efficiency and managerial performance based on empirical data. In this method, profit is modeled to be deviated from its profit efficient frontier which is influenced by the input and output functions. The sample of this study was 40 Islamic Commercial Banks and Islamic Business Units for 4 years starting from 2014-2018 which were analyzed using the STATA 8 technique with panel data. The results of this research show that Islamic banks are technically efficient based on intermediation (100%) and the efficient scale based on intermediation and production. Efficiency can be increasing liquid assets, while based on production approach.
Perancangan Aplikasi Kamus Istilah Jawa Berbasis Android sebagai Upaya Pelestarian Budaya Jawa Muljono, Muljono; Rokhman, Nur; Zeniarja, Junta; Nugroho, Raden Arief; Suryaningtyas, Valentina Widya; Aryanto, Bayu
ANDHARUPA: Jurnal Desain Komunikasi Visual & Multimedia Vol. 9 No. 04 (2023): December 2023
Publisher : Dian Nuswantoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/andharupa.v9i4.9282

Abstract

AbstrakSalah satu bahasa daerah di Indonesia yang paling beragam dan kaya kosakatanya adalah bahasa Jawa.  Namun, seringkali sulit bagi orang memahami arti istilah-istilah Jawa. Tujuan penelitian ini adalah untuk mengembangkan aplikasi kamus digital istilah Jawa yang akan membantu pengguna memahami dan menggunakan istilah Jawa.  Aplikasi dikembangkan dalam penelitian ini memungkinkan akses cepat dan mudah bagi pengguna dalam mencari istilah Jawa beserta definisi, contoh penggunaan, dan informasi terkait lainnya. Aplikasi ini dilengkapi fitur-fitur tambahan seperti pengucapan audio dan fitur urun daya yang memungkinkan masyarakat dapat menambah database tetapi tetap menunggu validasi dari pengelola aplikasi.  Dalam pengembangan aplikasi kamus digital ini menggunakan metode waterfall dan metode blackbox untuk metode pengujiannya.  Penelitian ini menghasilkan aplikasi kamus digital bahasa Jawa yang bernama "Senarai Istilah Jawa" yang bertujuan untuk membantu masyarakat memahami dan menggunakan istilah Jawa dan sebagai salah satu bentuk upaya membantu pelestarian bahasa daerah di Indonesia. Kata Kunci: aplikasi android, budaya, kamus istilah Jawa, metode waterfall AbstractOne of the regional languages in Indonesia that is most diverse and rich in vocabulary is Javanese. However, it is often difficult for people to understand the meaning of Javanese terms. The aim of this research is to develop a digital dictionary application of Javanese terms that will help users understand and use Javanese terms. The application developed in this research allows users quick and easy access to search for Javanese terms along with definitions, usage examples and other related information. This application is equipped with additional features such as audio pronunciations and a crowdsourcing feature that allows people to add to the database but still wait for validation from the application manager. In developing this digital dictionary application, the waterfall method and black box method were used for testing methods. This research produces a digital Javanese dictionary application called "Senarai Istilah Jawa" which aims to help people understand and use Javanese terms and as a form of effort to help preserve regional languages in Indonesia. Keywords: android application, culture, dictionary of Javanese terms, waterfall method 
Bagging Nearest Neighbor and its Enhancement for Machinery Predictive Maintenance Arisani, Muhammad Irfan; Muljono, Muljono
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8158

Abstract

K-nearest Neighbor is a simple algorithm in Machine learning for such a prediction classification task which plays in valuable aspects of understanding big data. However, this algorithm sometimes does a lacking job of classification tasks for many different dataset characteristics. Therefore, this study will adopt enhancement methods to create a better performance of the nearest-neighbor model. Thus, this study focused on nearest neighbor enhancement to do a binary classification task from the extremely unbalanced dataset of a machine failure problem. Firstly, this study will create new features from the machinery dataset through the feature engineering processes and transform the chosen numerical features with standardization steps as the proper scaling. Then, the modified under-sampling method will be given which will reduce the amount of the majority class to 4.75 times that of the minority class. Next is the applied grid-search tuning which will find the right parameter combinations for the nearest-neighbor model being applied. Furthermore, the previous pre-processing steps will be combined with an additional bagging method. Finally, the resulting bagged KNN will present a 0.971 rate of accuracy, 0.555 rate of precision, 0.781 rate of recall, 0.649 rate of f1-score, 0.95 auc of ROC curve, and 0.702 auc of precision-recall curve.
Machine Learning untuk Deteksi Stres Pelajar: Perceptron sebagai Model Klasifikasi Efektif untuk Intervensi Dini Zahrah, Febrina Nabila; Muljono, Muljono
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.28011

Abstract

Stress is a serious challenge for students that can negatively impact physical health, mental well-being, and academic performance. However, accurate and effective stress detection approaches to support early intervention are still limited. This study aims to evaluate machine learning models for detecting student stress levels with optimal accuracy to facilitate early intervention. The research employs a quantitative approach using a dataset containing 1,100 student samples from Nepal, encompassing 20 stress-related features from psychological, social, academic, environmental, and physiological aspects. Data were collected via a self-report questionnaire, processed with StandardScaler scaling, and analyzed using 10-fold cross-validation. The models tested include Perceptron, Gradient Boosting Trees Classifier (GBTC), Naive Bayes (NB), Logistic Regression (LR), and AdaBoost. The results show that Perceptron performed the best with an accuracy of 97.27%, followed by NB (95.45%), GBTC (94.54%), LR (94.54%), and AdaBoost (93.63%). Perceptron, with its advantage in linearity and evaluation through 10-fold cross-validation, shows great potential as an effective classification model for student stress detection, which can accelerate early intervention and enhance student well-being and learning environments.
Perbandingan Kinerja Metode Linear Regression, LSTM dan GRU Untuk Prediksi Harga Penutupan Saham Coco-Cola Silalahi, Rosalia Natal; Muljono, Muljono
Komputika : Jurnal Sistem Komputer Vol. 13 No. 2 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i2.12265

Abstract

In the stock market, making predictions about stock price movements is crucial for traders, as this will affect their potential profits or losses. The accuracy of the prediction results largely depends on the method used and the quality of the data available. Therefore, this research chooses the subject of predicting the stock price of Coco-Cola. This research will conduct a comparison between several different time series data analysis methods. These methods include Linear Regression, LSTM, and GRU. The comparison of the three methods with window-width variations of 3, 4, and 5 provides an in-depth insight into the performance of each model. The comparison results show that the model achieves the best performance when using window-width=3 in the Linear Regression method. Linear Regression with MSE of 0.24, RMSE of 0.49, shows better performance compared to LSTM (2.72 & 1.65) and GRU (0.31 & 0.55). This research provides valuable guidance for future predictive model development, with a focus on improving the accuracy and precision of stock price predictions.
Application of MobileNetV2 and SVM Combination for Enhanced Accuracy in Pneumonia Classification Meindiawan, Eka Putra Agus; Muljono, Muljono
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8426

Abstract

Pneumonia is a very common respiratory infection in low- and middle-income countries and is still a leading cause of death, especially among children under five years old. Modern technologies, such as machine learning, offer significant potential in improving the automatic detection of pneumonia through chest X-ray (CXR) image analysis. This study aims to develop a more accurate pneumonia diagnosis system by evaluating various feature extraction methods. CXR datasets of pneumonia patients were resized to 180x180 pixels and balanced using the SMOTE-Tomek technique. Three main approaches were investigated: direct classification using Support Vector Machine (SVM) on the SMOTE-Tomek balanced dataset, feature extraction using Sobel edge detection followed by SVM classification, and feature extraction using MobileNet-V2 followed by SVM classification. The results showed that Scheme 1 achieved 97% accuracy, Scheme 2 decreased to 95%, and Scheme 3 achieved the highest accuracy at 98%. The lower accuracy in Scheme 2 is due to the limitations of Sobel edge detection, which reduces the key features in the CXR image. On the other hand, the improvement in Scheme 3 is due to the effective feature extraction capability of MobileNet-V2. In conclusion, the choice of feature extraction method plays an important role in determining the accuracy of an automated diagnostic system. This study builds on existing research and is expected to make a significant contribution to the development of more accurate and efficient automated diagnostic systems, which can ultimately help reduce pneumonia-related mortality.
Pemanfaatan Google Site dalam Pelatihan Pembuatan Website Sebagai Kegiatan Penunjang Edukasi Life Skills Pelajar SMA N 2 Mranggen Kabupaten Demak Herowati, Wise; Kurniawan, Achmad Wahid; Budi, Setyo; Muljono, Muljono; Rustad, Supriadi; Ignatius Moses Setiadi, De Rosal; Sutojo, T.; Trisnapradika, Gustina Alfa; Aprihartha, Moch Anjas
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.v8i1.2840

Abstract

Menghadapi persaingan kemampuan dan keterampilan terutama untuk generasi sekarang harusnya dihadapi dengan mempersiapkan pengetahuan yang mumpuni terutama kemampuan-kemampuan untuk menunjang life skills . Kemampuan tersebut perlu diperkuat sedari diri terutama pada jenjang pendidikan menengah atas atau jenjang SMA. Salah satu kemampuan yang dapat diasah pada jenjang pendidikan tersebut adalah pengetahuan dan kemampuan mengenai pembuatan sebuah website. Menciptakan sebuah website sering kali dianggap sulit dan membutuhkan kemampuan pemrograman khusus, hal ini menjadi tantangan tersendiri salah satunya bagi salah satu sekolah yakni SMA N 2 Mranggen Demak. Sebagai salah satu cara menyelesaikan tantangan tersebut, kegiatan PKM yang telah terlaksana ini memperkenalkan konsep dasar pembuatan website menggunakan Google Site. Diharapkan melalui kegiatan pelatihan tersebut para pelajar dapat memiliki keterampilan tambahan untuk menambah kemampuan guna menunjang life skills mereka
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