Article Per Year (5 Year)
p-Index From 2021 - 2026
9.294
P-Index
This Author published in this journals
All Journal Jurnal Simetris Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer JUSIFO : Jurnal Sistem Informasi Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah KOMPUTASI Format : Jurnal Imiah Teknik Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Informatika Jurnal Komputasi JITK (Jurnal Ilmu Pengetahuan dan Komputer) IKRA-ITH Informatika : Jurnal Komputer dan Informatika Sebatik Jiko (Jurnal Informatika dan komputer) Astonjadro Simtek : Jurnal Sistem Informasi dan Teknik Komputer CCIT (Creative Communication and Innovative Technology) Journal Journal of Information System, Applied, Management, Accounting and Research Informatika IJITEE (International Journal of Information Technology and Electrical Engineering) Journal of Applied Science, Engineering, Technology, and Education JUKI : Jurnal Komputer dan Informatika Jurnal Abdidas International Journal of Industrial Optimization (IJIO) Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal Teknik Informatika (JUTIF) International Journal Of Science, Technology & Management (IJSTM) Journal of Technology and Informatics (JoTI) Indonesian Journal of Multidisciplinary Science Journal Of World Science Buletin Sistem Informasi dan Teknologi Islam Jurnal Locus Penelitian dan Pengabdian Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Ilmu Multidisplin Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Jurnal Indonesia Sosial Teknologi Jurnal Indonesia Sosial Sains Journal Research of Social Science, Economics, and Management Eduvest - Journal of Universal Studies Kohesi: Jurnal Sains dan Teknologi SmartComp Jurnal Informatika Polinema (JIP) Asian Journal of Social and Humanities Paradigma: Jurnal Filsafat, Sains, Teknologi, dan Sosial Budaya
Akbar, Habibullah
Universitas Esa Unggul
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering 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 Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Public Health Social Sciences Transportation Other

Published : 79 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 79 Documents
Search

 4   5   6   7   8 
Coffee Grind Size Detection By Using Convolutional Neural Network (CNN) Architecture Franky Leonard; Akbar, Habibullah
Journal of Applied Science, Engineering, Technology, and Education Vol. 4 No. 1 (2022)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (734.48 KB) | DOI: 10.35877/454RI.asci842

Abstract

Identification of coffee grinder results is one of the needs to support the government and coffee shop UMKM to innovate coffee drink products. Special expertise and sufficient time are needed to process the identification of coffee grinder results in the laboratory. Several previous research methodologies, the process of identifying the results of coffee grinders is still manual with human visuals. While the use of a computer system is obtained from cross-sectional images of coffee grounds using microscopic and macroscopic processes. Currently, computer vision and machine learning technologies have been developed to identify various types of objects, one of which is coffee objects. This study contributes in classifying several classifications of coffee grinder results using Convolutional Neural Networks (CNN). The novelty of this research lies in improvising the optimal CNN parameters in detecting objects from a coffee grinder. The proposed AlexNet architecture has seven layers, namely three convolution layers, two max-pooling layers and two Hidden Layers for the five characters of the coffee grinder image dataset.dataset private resulting from a coffee grinder of 1039 items and an augmentation process to make it more optimal and prevent overfitting , the test first changes the input image to 50 x 50, 100 x 100, and 150 x 150 pixels and each repeats at 250 epochs. The experimental results show that AlexNet with parameters batchsize 8, Learning Rate 0.001, Optimizer SGD, training and splitting data ratio 0.6:0.4 and balanced data typehasan accuracy validation value reaching 95%.
Studi Komparasi Naive Bayes, K-Nearest Neighbor, dan Random Forest untuk Prediksi Calon Mahasiswa yang Diterima atau Mundur Sejati, Puteri; Munawar, Munawar; Pilliang, Marzuki; Akbar, Habibullah
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 7: Spesial Issue Seminar Nasional Teknologi dan Rekayasa Informasi (SENTRIN) 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022976737

Abstract

Penelitian ini bertujuan untuk mendapatkan model prediksi terbaik dari data Penerimaan Mahasiswa Baru tahun 2014 hingga 2019 dengan membandingkan Naive Bayes, K-Nearest Neighbor, dan Random Forest. Penelitian ini menggunakan metode klasifikasi untuk memprediksi calon mahasiswa. Mereka diterima atau  mundur. Dalam penelitian ini digunakan 19.603 data latih dan 4.901 data uji. Hasil penelitian menunjukkan bahwa algoritma Random Forest adalah yang terbaik dengan akurasi 73,61%, dibandingkan dengan K-Nearest Neighbor dengan akurasi 72,08%, dan Naive Bayes dengan akurasi 70,47%. Disimpulkan juga bahwa optimasi model dengan teknik Hyperparameter menghasilkan nilai akurasi yang lebih baik. Hasil penelitian ini dapat digunakan untuk mendukung bagian pemasaran dalam meminimalisir jumlah calon mahasiswa yang mengundurkan diri. AbstractThis study aimed to obtain the best predictive model from New Student Admissions data for 2014 to 2019 by comparing Naive Bayes, K-Nearest Neighbor, and Random Forest. This study used the classification method to predict prospective students. They are accepted or withdrawn. In this study, 19,603 training data and 4,901 test data were used. The results showed that the Random Forest algorithm was the best with an accuracy of 73.61%, compared to K-Nearest Neighbor with an accuracy of 72.08%, and Naive Bayes with an accuracy of 70.47%. It is also concluded that optimizing the model with the Hyperparameter technique produces better accuracy values. This study's results can be used to support the marketing department in minimizing the number of withdrawn prospective students.
Analisis dan Design Knowledge Management System pada PT XYZ dengan Menggunakan Metode Tiwana Alexander, Alexander; Firmansyah, Gerry; Tjahjono, Budi; Mulyo Widodo, Agung; Akbar , Habibullah
Jurnal Locus Penelitian dan Pengabdian Vol. 4 No. 8 (2025): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v4i8.4686

Abstract

Dalam dunia bisnis, keberlangsungan dan pertumbuhan suatu perusahaan sangat dipengaruhi oleh kualitas sumber daya manusia (SDM) atau karyawan yang dimiliki, karyawan yang kompeten dan memiliki wawasan pengetahuan yang luas dianggap sebagai aset penting yang dapat menjadi pembeda utama antara perusahaan dengan para pesaingnya. Maka dari itu, perusahaan biasanya berinisiatif untuk melakukan berbagai macam program pelatihan dan pengembangan yang bertujuan untuk meningkatkan kemampuan dan pengetahuan karyawannya secara berkelanjutan, hal lain yang dapat dilakukan oleh perusahaan adalah dengan mengelola pengetahuan dari karyawannya melalui sebuah tools yaitu Knowledge Management System (KMS). Sebagai perusahaan konsultan properti terkemuka dengan jaringan global yang luas, PT XYZ memiliki komitmen untuk dapat mengembangkan pengetahuan karyawannya, PT XYZ selalu berupaya untuk menciptakan lingkungan kerja yang mendukung pertumbuhan pengetahuan, terutama bagi karyawan di bidang Information and Technology (IT). Penelitian ini menggunakan metode Tiwana yang dilakukan hingga tahapan ke-6 dari 10 tahapan yang ada, dengan tujuan penelitian untuk menghasilkan blueprint sebagai landasan untuk merancang KMS yang sesuai dengan kebutuhan perusahaan. Adapun hasil dari penelitian ini adalah sebuah blueprint rancangan KMS.
Pengembangan Model IT Masterplan untuk Perguruan Tinggi: Studi Kasus Pada Universitas Bakrie dengan Pendekatan TOGAF Wijaya, Jacob S; Firmansyah, Gerry; Tjahjono, Budi; Akbar, Habibullah
Jurnal Locus Penelitian dan Pengabdian Vol. 4 No. 11 (2025): JURNAL LOCUS: Penelitian dan Pengabdian
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/locus.v4i11.4859

Abstract

Transformasi digital di perguruan tinggi menuntut perencanaan teknologi informasi yang strategis, terstruktur, dan berkelanjutan agar mampu mendukung efisiensi akademik dan administrasi. Penelitian ini bertujuan untuk menyusun IT Masterplan Universitas Bakrie sebagai kerangka strategis pengembangan sistem informasi dan transformasi digital universitas secara holistik. Metode penelitian menggunakan pendekatan kualitatif dengan kombinasi analisis dokumen, wawancara semi-terstruktur, dan survei di lingkungan Universitas Bakrie, termasuk Biro Rektorat, Direktorat TI, dan fakultas-fakultas. Analisis dilakukan dengan mengacu pada kerangka kerja Enterprise Architecture (EA) menggunakan TOGAF Architecture Development Method (ADM) serta analisis kesenjangan (gap analysis) antara kondisi eksisting dan target arsitektur yang diinginkan. Hasil penelitian menunjukkan bahwa kondisi sistem TI Universitas Bakrie masih terfragmentasi, dengan beberapa aplikasi tidak saling terintegrasi dan data yang tersebar. Rekomendasi utama berupa rancangan arsitektur target yang menerapkan model berbasis layanan (service-oriented architecture) dan integrasi melalui middleware untuk mendukung pertukaran data lintas unit secara real-time. Selain itu, disusun pula desain data warehouse terpusat serta mekanisme tata kelola data (data governance) untuk meningkatkan akurasi dan analitik lintas fungsi. Implementasi IT Masterplan ini diharapkan mampu meningkatkan efisiensi pengelolaan sumber daya TI, mengurangi redundansi sistem, memperkuat interoperabilitas aplikasi, serta mendorong kelincahan digital universitas dalam menghadapi era transformasi pendidikan tinggi.
SENTIMENT ANALYSIS FOR E-COMMERCE PRODUCT REVIEWS BASED ON FEATURE FUSION AND BIDIRECTIONAL LONG SHORT-TERM MEMORY Akbar, Habibullah; Aryani, Diah; Mohammed Al-shammari, Marwan Kadhim; Ulum, M. Bahrul
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2675

Abstract

E-commerce platforms would benefit from performing sentiment analysis of their customer's feedback. However, the vast amount of transaction data makes manual sentiment analysis of product reviews impractical. This research proposes an approach to automatically classify the sentiment of a given product review based on three major steps: data preprocessing, text representation, and classification model development. First, review data is cleaned to remove ambiguity and non-meaningful elements. Second, Word2Vec and GloVe features are combined to represent the words in a more unified vector space. Lastly, these combined features are classified to determine sentiment polarity using the Bidirectional Long Short-Term Memory Network (BiLSTM) model. The test results demonstrate that the proposed BiLSTM model achieves 91% uniform performance for all four metrics (accuracy, precision, recall, and F1-score), which is 3% higher than the results achieved by the standard LSTM model. Moreover, the BiLSTM model requires 9.91 seconds less training computation time than the LSTM.
Integration Of Garch Models And External Factors In Gold Price Volatility Prediction: Analysis And Comparison Of Garch-M Approach Tardiana, Arisandi Langgeng; Akbar, Habibullah; Firmansyah, Gerry; Widodo, Agung Mulyo
Eduvest - Journal of Universal Studies Vol. 4 No. 5 (2024): Journal Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v4i5.1195

Abstract

This study investigates the volatility of gold prices by applying the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and extending it with the GARCH-M model, incorporating the Federal Reserve's interest rate as an external variable. The GARCH(1,1) model revealed a positive average daily return for gold, with high sensitivity to recent price changes, indicated by the significant estimation of mu and a high alpha1 value. The persistence of past volatility on current volatility is reflected by a beta1 value close to one. In the GARCH-M model development, a significant negative relationship was found between the Federal Reserve's interest rates and gold returns, suggesting that an increase in the Federal Reserve's interest rates could potentially decrease gold returns. An increase in the Log Likelihood value and improvements in information criteria such as the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) indicate that the GARCH-M model provides a better fit than the GARCH(1,1) model that uses only gold price data. The study concludes that macroeconomic factors like the Federal Reserve's interest rates play a crucial role in influencing gold price volatility, and these findings can aid investors and portfolio managers in devising more effective risk management strategies. Additionally, the findings contribute to financial theory by highlighting the importance of multivariate models in the analysis of asset price volatility.
Product Recommendations Using Adjusted User-Based Collaborative Filtering on E-Commerce Platforms Tartila, Gilang Romadhanu; Akbar, Habibullah; Firmansyah, Gerry; Widodo, Agung Mulyo
Eduvest - Journal of Universal Studies Vol. 5 No. 1 (2025): Journal Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i1.50224

Abstract

Product recommendations on e-commerce platforms play a crucial role in supporting customers' purchasing decisions by leveraging user data to provide relevant product suggestions. With the increasing volume of e-commerce data, recommendation methods are needed that are not only accurate but also capable of being applied to diverse datasets. This research focuses on evaluating three product recommendation methods, namely User-Based Collaborative Filtering, Item-Based Collaborative Filtering, and Content-Based Filtering, using various datasets from the Kaggle platform, including transaction data and user reviews. The main problem identified is how to ensure that these three recommendation methods remain optimal despite using different datasets. Through an experimental approach, this research aims to implement and evaluate the performance of these recommendation methods. The results of this study are expected to demonstrate that one of the recommendation methods can work generally on various datasets, thereby making a significant contribution to the selection of the appropriate product recommendation method on e-commerce platforms.
Evaluation Of It System Operational Services Using The Itil Framework In The Service Desk Domain (A Case Study Of PT Erafone Dotcom) Nainggolan, Restamauli br; Tjahjono, Budi; Widodo, Agung Mulyo; Akbar , Habibullah
Eduvest - Journal of Universal Studies Vol. 5 No. 8 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i8.51908

Abstract

PT Erafone dotcom is one of the mobile phone and tablet retailer companies in Indonesia from various well-known brands. PT Erafone dotcom uses the service desk as an after-sales support system service for customers or users in the smooth transaction process. The current problem with service desk services is the slow response to handling and resolving obstacles. Evaluation is needed to be able to improve operational services. The Information Technology Infrastructure Library (ITIL) V4 will be used to evaluate service desk services in IT Operational at PT Erafone Dotcom. The purpose of this study is to evaluate IT Support in operational services using the ITIL V4 framework with 2 practices in the domain of General Management Practice and 5 practices in the domain of Service Management Practice. The results of this study are that the level of service in IT Operational and the level of capability are at level 3 (Defined), which means that IT Operational support to users has run optimally referring to management practice procedures and response to incidents. To increase the value of IT Operational support from the maturity level to match expectations and can improve management. The recommendation for improvement is that even though it is at level 3, there is still a gap in the practices used so that it is necessary to improve the recording of incidents and problems that occur, so that they can be analyzed and identified to help handle and prevent the recurrence of incidents and problems.
Qur’an Recitation Correction System Using Deepspeech Mahmudin, Hajon Mahdy; Akbar, Habibullah
Indonesian Journal of Multidisciplinary Science Vol. 2 No. 11 (2023): Indonesian Journal of Multidisciplinary Science
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/ijoms.v2i11.638

Abstract

The purpose of this study was to compare the performance of the two types of models used in the task of classifying Quran verses based on audio similarity. The first model is Model B which uses MFCC features and the MaLSTM architecture, while the second model is Model C, which is Model B with additional delta features. The stages in this study consist of determining the dataset, determining the parameters, preprocessing, training, and testing. The dataset in this study was obtained from the local dataset https://sahabatibadah.com/fasih/. This study conducted data analysis based on 172,895 samples of Al-Quran recitation sounds from Juzz 30, which includes a total of 37 surahs with 564 verses. This sound data were taken from the recording on the Qara'a application and collected from 500 users of the application. In this study, 3 out of 500 users were used as training data to train speech recognition models, while one user was used as testing data. The training model used was DeepSpeech supported by TensorFlow. In the model training process, 30% of the samples were used as a validation set. Based on the results, Model B with the MFCC feature is the best model in the task of recognizing and classifying audio-based Quran verses. The use of the delta feature in Model B and Model C show a negative impact on model performance. The MFCC feature is more recommended in the recognition and classification of audio-based Qur’an verses, especially in the LSTM model architecture.
Performance Analysis of Provider and Riverpod State Management Library on Flutter Applications Puryanto, Jonathan Aditya; Akbar, Habibullah
Journal of Technology and Informatics (JoTI) Vol. 7 No. 2 (2025): Vol. 7 N. 2 (2025)
Publisher : Universitas Dinamika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37802/joti.v7i2.1164

Abstract

State management libraries are essential components in Flutter app development. This research aims to compare the performance of the state management library Provider and its successor, Riverpod, to assist Flutter developers in choosing the right solution. Two versions of the MovieDB app were built, each utilizing Provider and Riverpod. Performance testing was conducted using three metrics: CPU Utilization, Memory Usage, and Execution Time, across three data volumes (1,000, 5,000, and 10,000). The results showed that CPU Utilization varied by only 0.1–0.2% with Riverpod being slightly more efficient at 1,000 and 10,000 data volumes. Execution Times also showed minimal differences, with Riverpod being marginally faster by approximately 0.01 seconds at 5,000 and 10,000 data volumes. Riverpod excelled in Memory Usage, demonstrating an average reduction of about 3–6% across all data volumes, particularly at higher data volumes. In conclusion, the performance of both libraries is fundamentally similar, but Riverpod is offers better memory efficiency and architectural flexibility. Therefore, Riverpod is recommended for new projects, while Provider remains a viable option for stable existing applications that already use it.
Co-Authors Adi Widiantono Agus Satriawan Aisyah, Zhavira Alexander Alexander, Alexander Alvin Barata Amelia Sholikhaq Andini, Ketrin Vani Andriana, Dian Andriyanti Asianto Anwar Nasihin Ardiansyah, Miri Ari Pambudi Arif Pami Setiaji Asianto, Andriyanti Astamar Putra, Ichlasul Fikri Azizah, Anik Hanifatul Bob Tjahjono Budi Tjahjono Calvin Ramadhani Alfahrezi Chiuman, Felix Delio, Ferdinand Defin Deni Pamungkas Gelantoro Putra Diah Aryani, Diah Dodo, La Dudy Fathan Ali Dwi Pamungkas, Eric Dwiputra, Dedy Elvaret Eric Dwi Pamungkas Fathan Ali, Dudy Fatonah, Nenden Siti Franky Leonard Gerry Firmansyah Gerry Firmansyah Gilang Banuaji Hadi, Muhammad Abdullah Hafizah Safira Kaurani Hani Dewi Ariessanti Haryoto, Iin Sahuri Hendy Hendy Herwanto, Agus Husni Sastra Mihardja Husni Satra Mihardja Husni Satra Mihardja Indri Handayani, Indri Intan Setya Palupi La Dodo Latumapayahu, Febrian Firmansyah Mahmudin, Hajon Mahdy Martin Saputra, Martin Marwan Kadhim Mohammed Al-shammari Marzuki Pilliang Mochamad Wahyudi Mochamad Welly Rosadi Mohamad Yusuf Mohammed Al-shammari, Marwan Kadhim Muhamad Septian Nugraha Muhammad Fajrul Aslim Muhammad Yusuf Morais Mukhamad Abduh MUNAWAR Munawar Munawar Nainggolan, Restamauli br Nanna Suryana Herman Narul Sakron Nasihin, Anwar Nenden Siti Fatonah Nenden Siti Fatonah Nenden Siti Fatonah Nila Rusiardi Jayanti Nizirwan Anwar Noviandi Noviandi Noviandi Noviandi, Noviandi Nugroho Budhisantosa Nugroho, Irfan Hari Pilliang, Marzuki Prabowo, Ary Pramesty, Feranti Destina Puryanto, Jonathan Aditya Putra, Sipky Jaya Rachman, Riyandi Patu Ramadhan, Noval Rizky Randy Swandy Reyhan, Athallah Rifqi Adi Prasetya Rizky Yananda Rosnanto, Imam Rudi Heri Marwan Rudy Setiawan Sabri Alim Sakron, Narul Sandfreni, Sandfreni Saputra, Rahdian Sea, Rona Aulia Wangsa Sejati, Puteri Setiawati, Popong Sfenrianto Sfenrianto Sinaga, Matius Eliezer Suardana, Made Aka Suhandi Junaedi Supriyade Supriyade Supriyade, Supriyade Sutanto, Imam Syahrizal Dwi Putra Syahrizal Dwi Putra Tantrisna, Ellen Tardiana, Arisandi Langgeng Tartila, Gilang Romadhanu Trenggana Natadirja Ulum, M. Bahrul Ulum, Muhamad Bahrul Widodo , Agung Mulyo Widodo, Agung Mulyo Wijaya, Jacob S Yaya Sudarya Triana