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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Media Statistika Jurnal Penelitian Karet Jurnal Teknologi dan Sistem Komputer Journal of Mathematics and Mathematics Education (JMME) Indonesian Journal of Electrical Engineering and Computer Science Nusantara Science and Technology Proceedings Jambura Journal of Biomathematics (JJBM) Jurnal Ilmiah Matematika Annals of Mathematical Modeling Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) Eduvest - Journal of Universal Studies Annals of Mathematical Modeling
Bustamam, Alhadi
Departemen Matematika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Indonesia
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Neuroscience Social Sciences Other

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Implementasi Reccurent Neural Network Untuk Memprediksi Harga Saham Harlianto, Didi; Rachardi, Andris; Rusdah, Deandra Aulia; Safitri, Egi; Sudarsono, Ely; Bustamam, Alhadi
Jurnal Teknologi dan Sistem Komputer 2021: Publication In-Press
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2021.13898

Abstract

Saham adalah instrumen investasi dengan harga yang sangat fluktuatif. Harga saham dalam kurun waktu tertentu membentuk suatu data runtun waktu. Saat ini, salah satu metode yang cukup populer untuk menangani data runtun adalah Recurrent Neural Network (RNN). Tulisan ini membahas penerapan RNN di masa yang akan datang dalam memprediksi harga saham berdasarkan data harga saham beberapa tahun ke belakang. Tetapi RNN standar memiliki kelemahan yaitu terjadinya kondisi vanishing gradient. Oleh karena itu, arsitektur Long Short Term Memory (LSTM) digunakan pada RNN untuk mengatasi masalah tersebut. Sebagai pembanding, ditampilkan pula hasil prediksi dengan menggunakan model RNN standar. Hasilnya, RNN dengan arsitektur LSTM dapat dengan baik memprediksi harga saham dibandingkan RNN standar yang direfleksikan oleh nilai Mean Absolute Error (MAE) antar kedua model.
Performance of multivariate mutual information and autocorrelation encoding methods for the prediction of protein-protein interactions Alhadi Bustamam; Mohamad Irlin Sunggawa; Titin Siswantining
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 2: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i2.pp773-786

Abstract

Protein interactions play an essential role in the study of how an organism can be infected with a disease and also its effects. One of the challenges in computational methods in the prediction of protein-protein interactions is how to represent a sequence of amino acids in a vector so that it can be used in machine learning to create a model that can predict whether or not an interaction occurs in a protein pair. This paper examined the qualitative feature encoding methods of amino acid sequence, namely, multivariate mutual information (MMI), and the quantitative feature encoding methods, namely, autocorrelation. We develop the new design for MMI and autocorrelation feature encoding methods which give better results than the previous research. There are four ways to build the MMI method and six ways to build the autocorrelation method that we tested. We also built four types of MMI-autocorrelation (mixed) method and look for the best form of each type of MMI, autocorrelation, and mixed-method. We combine these feature encoding methods with support vector machine (SVM) as machine learning methods. We also test the encoding methods we propose to several machine learning classifier methods, such as random forest (RF), k-nearest neighbor (KNN), and gradient boosting.
Analysis of diabetes mellitus gene expression data using two-phase biclustering method Rahmat Al Kafi; Alhadi Bustamam; Wibowo Mangunwardoyo
Jurnal Ilmiah Matematika Vol 8, No 2 (2021)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/konvergensi.v0i0.22111

Abstract

The purpose of this research is to find bicluster from Type 2 Diabetes Mellitus genes expression data which samples are obese and lean people using two-phase biclustering. The first step is to use Singular Value Decomposition to decompose matrix gene expression data into gene and condition based matrices. The second step is to use K-means to cluster gene and condition based matrices, forming several clusters from each matrix. Furthermore, the silhouette method is applied to determine the number of optimum clusters and measure the accuracy of grouping results. Based on the experimental results, Type 2 Diabetes Mellitus dataset with 668 selected genes produced optimal biclusters, with six biclusters. The obtained biclusters consist of 2 clusters on the gene-based matrix and 3 clusters on the sample-based matrix with silhouette values, respectively, are 0.7361615 and 0.7050163.
Lung cancer classification based on support vector machine-recursive feature elimination and artificial bee colony Alhadi Bustamam; Zuherman Rustama; Selly A. A. K; Nyoman A. Wibawa; Devvi Sarwinda; NadyaAsanul Husna
Annals of Mathematical Modeling Vol 3, No 1 (2021)
Publisher : Research and Social Study Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33292/amm.v13i1.71

Abstract

Early detection of cancerous cells can increase survival rates for patients by more than 97%. Microarray data, used for cancer classification, are comp osed of many thousands of features and from tens to hundreds of instances. Handling these huge datasets is the most imp ortant challenge in data classification. Feature selection or reduction is therefore an essential task in data classification. We prop ose a cancer diagnostic to ol using a supp ort vector machine for classifier and feature selection. First, we use supp ort vector machine-recursive feature elimination to prefilter the genes. This was enhanced with the artificial b ee colony algorithm. We ran four simulations using Ontario and Michigan lung cancer datasets. This approach provides higher classification accuracy than those without feature selection, supp ort vector machine-recursive feature elimination, or the artificial b ee colony algorithm. The accuracy of a supp ort vector machine using a feature selection-based recursive feature elimination metho d combined with the artificial b ee colony algorithm reached 98% with 100 b est features for the Michigan lung cancer dataset and 97% with 70 b est features for the Ontario lung cancer dataset. SVM with RFE-ABC as the feature selection metho d gives us an accurate result to diagnose Lung cancer using microarray data.
Diabetic Retinopathy Detection Using GoogleNet Architecture of Convolutional Neural Network Through Fundus Images Amnia Salma; Alhadi Bustamam; Devvi Sarwinda
Nusantara Science and Technology Proceedings Bioinformatics and Biodiversity Conferences (BBC)
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2021.0701

Abstract

The number of people who have Diabetes is about 422 million in the world. Diabetes is a group of metabolic disease characterized by elevated lev- els of blood glucose. The serious damage of blood vessels caused by Diabetes in the tissue at the retina is called Diabetic Retinopathy. Diabetic Retinopathy can cause severe blindness. Early detection can help patients find a suitable treatment and prevent the blindness. Opthalmologists can detect this disease by screening, but this method takes a long time, is very costly and need pro- fessional skills to perform it. In the big data era, many researchers use deep learning models for medical help. One of the models use image classification. We have designed a tool using image classification to help ophthalmologists detect diabetic retinopathy. In this research, we use image classification to classify Diabetic Retinopathy into two classes which are normal (No DR) and Diabetic Retinopathy. We use 200 datasets of fundus images that we obtain from Kaggle Database. We used deep learning model in this research that is one of Convolutional Neural Network architecture called GoogleNet. For training the model we used Python as programming languange with Pytorch library. GoogleNet has a very good performance for image classification and has an accuracy of 88%.
Measuring the accuracy of LSTM and BiLSTM models in the application of artificial intelligence by applying chatbot programme Prasnurzaki Anki; Alhadi Bustamam
Indonesian Journal of Electrical Engineering and Computer Science Vol 23, No 1: July 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v23.i1.pp197-205

Abstract

Python programme contains a question and answer system that derived from data sets that have used and implemented the chatbot in this modern era. where the data collected is in the form of corpuses containing extensive metadata-rich fictional conversations derived from extracted film scripts, commonly called cornell movie dialogue corpus. The various models have been used chatbots in python programmes, and LSTM and BiLSTM models were specifically used in this study. Where the form of accuracy will be reported as a result of the implementation of LSTM and BiLSTM models in the chatbot programme. The programme performance will be influenced by the data from the model selection, because the level of accuracy is determined by the target programme being taken. So this is the main factor that determines which model to choose. Based on considerations required for choosing the programme model, in the end the LSTM and the BiLSTM models are chosen and will be applied to the programme. Based on the LSTM and BiLSTM chatbot programmes that have been tested, it can be concluded that the best parameters come from a pair of BiLSTM chatbots using the BiLTSM model with an average accuracy value of 0.995217.
MODEL KLASTERING SKM3 (SUBCONTROLLED K-MEANS MAX-MIN) DAN APLIKASINYA DALAM MENGHITUNG ELEKTABILITAS PASANGAN CALON KEPALA DAERAH Patuan P Tampubolon; Tesdiq Prigel Kaloka; Olivia Swasti; Widya Fajar Mustika; Alhadi Bustamam
Journal of Mathematics and Mathematics Education Vol 8, No 2 (2018): Journal of Mathematics and Mathematics Education (JMME)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jmme.v8i2.25838

Abstract

Abstract: Indonesia is a legal state that chooses a leader based on the results of general elections, such as the election of presidents and regional leaders. Electability is statistical data for each pair of candidates who show public interest to choose the candidate. Electability data is usually obtained from the results of questionnaires or interviews with constituents. The data search process is carried out by a survey institution. Most people discuss voluntarily in social media related to the candidate that they will choose. This study uses discussion data from social media to calculate the electability of each pair of candidates by using cluster method. The cluster method is K-Means. K-Means employs euclidean distance to determine the cluster of each data, while the number of cluster can be determined by the user. This study proposes SKM3 model (Subcontrolled K-Means Max-Min), which applies the minimum and maximum average values to decide the cluster of each data. SKM3 cluster is controlled by K-Means method that uses Euclidian distance. SKM3 model is processed using news data from detik.com site for the election of regional leader of West Java, Central Java, and East Java. The error value of SKM3 model is calculated through RMSE (Root Mean Square Error). The error value of West Java is 0.0452, the error value of Central Java up to 0.0343, and the error value of East Java is 0.2382. Based on the error values of each electoral region, it shows that SKM3 model has a small error value, so it can be concluded that SKM3 model is good for calculating the electability of the leader by using clustering method.Keywords:Electability, Clustering, K-Means, SKM3.
Lung cancer classification based on support vector machine-recursive feature elimination and artificial bee colony Alhadi Bustamam; Zuherman Rustam; Selly A. A. K; Nyoman A. Wibawa; Devvi Sarwinda; Nadya Asanul Husna
Annals of Mathematical Modeling Vol. 3 No. 1 (2023)
Publisher : Research and Social Study Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33292/amm.v3i1.26

Abstract

Early detection of cancerous cells can increase survival rates for patients by more than 97%. Microarray data, used for cancer classification, are comp osed of many thousands of features and from tens to hundreds of instances. Handling these huge datasets is the most imp ortant challenge in data classification. Feature selection or reduction is therefore an essential task in data classification. We prop ose a cancer diagnostic to ol using a supp ort vector machine for classifier and feature selection. First, we use supp ort vector machine-recursive feature elimination to prefilter the genes. This was enhanced with the artificial b ee colony algorithm. We ran four simulations using Ontario and Michigan lung cancer datasets. This approach provides higher classification accuracy than those without feature selection, supp ort vector machine-recursive feature elimination, or the artificial b ee colony algorithm. The accuracy of a supp ort vector machine using a feature selection-based recursive feature elimination metho d combined with the artificial b ee colony algorithm reached 98% with 100 b est features for the Michigan lung cancer dataset and 97% with 70 b est features for the Ontario lung cancer dataset. SVM with RFE-ABC as the feature selection metho d gives us an accurate result to diagnose Lung cancer using microarray data.
The hybrid of BERT and deep learning models for Indonesian sentiment analysis Dwi Guna Mandhasiya; Hendri Murfi; Alhadi Bustamam
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp591-602

Abstract

Artificial intelligence (AI) is one example of how data science innovation has advanced quickly in recent years and has greatly improved human existence. Neural networks, which are a type of machine learning model, are a fundamental component of deep learning in the field of AI. Deep learning models can carry out feature extraction and classification tasks in a single design because of their numerous neural network layers. Modern machine learning algorithms have been shown to perform worse than this model on tasks including text classification, audio recognition, imaginary, and pattern recognition. Deep learning models have outperformed AI-based methods in sentiment analysis and other text categorization tasks. Text data can originate from a number of places, including social media. Sentiment analysis is the computational examination of textual expressions of ideas and feelings. This study employs the convolutional neural network (CNN), long-short term memory (LSTM), CNN-LSTM, and LSTM-CNN models in a deep learning framework using bidirectional encoder representations from transformers (BERT) data representation to assess the performance of machine learning. The implementation of the model utilises YouTube discussion data pertaining to political films associated with the Indonesian presidential election of 2024. Confusion metrics, including as accuracy, precision, and recall, are then used to analyse the model’s performance.
DISTRIBUSI SPASIAL KESEHATAN TANAMAN KARET MENGGUNAKAN SENTINEL-1 Ayu, Farida; Riesnandar, Ariq Anggaraksa; Manessa, Masita Dwi Mandini; Supriatna, Supriatna; LESTARI, Retno; Bustamam, Alhadi; Sarwinda, Devvi; Stevanuse, Charlos Togi; Efriana, Anisya Feby
Jurnal Penelitian Karet JPK : Volume 42, Nomor 1, Tahun 2024
Publisher : Pusat Penelitian Karet - PT. Riset Perkebunan Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22302/ppk.jpk.v42i1.881

Abstract

Tanaman karet (Hevea brasiliensis) merupakan komoditas penting yang menjadi sumber pendapatan petani di Indonesia. Namun, dalam beberapa tahun terakhir perkebunan karet di Indonesia mengalami penurunan mutu dan produksi yang disebabkan oleh penyakit gugur daun Pestalotiopsis sp. Teknologi remote sensing dapat menjadi solusi dalam pemantauan kesehatan tanaman. Kendala tutupan awan dalam pemantauan perkebunan karet menggunakan citra optik menghambat keberlangsungan. Citra Sentinel-1 dilengkapi data Synthetic Aperture Radar (SAR) yang mampu untuk menembus awan. Sehingga, penelitian ini bertujuan untuk menganalisis distribusi spasial kesehatan tanaman dengan menggunakan multi indeks vegetasi RVI dan NDRVI pada citra Sentinel-1. Hasil penelitian menunjukan bahwa multi indeks vegetasi tidak memiliki hubungan yang signifikan dengan kelas kesehatan tanaman. Faktor noise, panjang gelombang, dan hamburan balik mengindikasikan rendahnya hubungan antar variabel.
Co-Authors AA Sudharmawan, AA Adnan, Risman Amnia Salma Arry Yanuar Ayu, Farida Devvi Sarwinda Devvi Sarwinda Dian Maharani Dwi Guna Mandhasiya Efriana, Anisya Feby Harlianto, Didi Hendri Murfi Kafi, Rahmat Al Kartika Chandra Dewi MASITA DWI MANDINI MANESSA, MASITA DWI MANDINI Mastika, Mastika Maulana, Irvan Mohamad Irlin Sunggawa Muhammad Ridho Nadya Asanul Husna NadyaAsanul Husna Nugroho, Trianto Haryo Nyoman A. Wibawa Nyoman A. Wibawa Olivia Swasti Patuan P Tampubolon Prasnurzaki Anki Rachardi, Andris Rahmat Al Kafi Rahmat Al Kafi Retno Lestari Riesnandar, Ariq Anggaraksa Rusdah, Deandra Aulia Safitri, Egi Sarwinda, Devvi Selly A. A. K Selly Anastassia Amellia Kharis Siswantining, Titin Stevanuse, Charlos Togi SUDARSONO, ELY Supriatna Supriatna Sutriyo Sutriyo, Sutriyo Tesdiq Prigel Kaloka Titin Siswantining WIBOWO MANGUNWARDOYO Wibowo Mangunwardoyo Widya Fajar Mustika Yuni Rosita Dewi Zuherman Rustam