Article Per Year (5 Year)
p-Index From 2021 - 2026
3.237
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) JIK Jurnal Ilmu Komputer Bulletin of Electrical Engineering and Informatics Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal Teknologi Informasi dan Ilmu Komputer Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Informatika Universitas Pamulang Jurnal Pengabdian Masyarakat AbdiMas KOMPUTEK CCIT (Creative Communication and Innovative Technology) Journal Poltekita : Jurnal Ilmu Kesehatan Jurnal Ilmu Manajemen Terapan (JIMT) Al-Istinbath: Jurnal Hukum Islam Quantum Wellness : Jurnal Ilmu Kesehatan Jurnal Ilmu Komputer dan Informatika Harmony Management: International Journal of Management Science and Business Global Management: International Journal of Management Science and Entrepreneurship Paradigma: Jurnal Filsafat, Sains, Teknologi, dan Sosial Budaya Jurnal Komputer, Informasi dan Teknologi
Munawar
Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Esa Unggul
Religion Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

Published : 33 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 33 Documents
Search

 1   2   3   4 
Comparative Analysis of Time Series Methods LSTM and ARIMA for Predicting Inventory Availability (Case Study: PT XYZ) Kartawijaya, Edi; Munawar, Munawar; Firmansyah, Gerry; Tjahjono, Budi
CCIT (Creative Communication and Innovative Technology) Journal Vol 18 No 1 (2025): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v18i1.3443

Abstract

Product availability plays a crucial role in supply chain management, directly impacting all aspects of business operations, from production to distribution. This study analyzes the optimization of product availability at PT. XYZ, a frozen and chilled food trading company in Indonesia, focusing on four main commodities: beef, buffalo meat, chicken, and potatoes. Utilizing historical transaction data from 2020 to July 27, 2024, this research compares the performance of two forecasting models: ARIMA (AutoRegressive Integrated Moving Average) and Long Short-Term Memory (LSTM), in predicting product availability The traditional ARIMA model has proven effective in time series data analysis but has limitations in capturing complex patterns and non-linear fluctuations. LSTM, as a machine learning technique, demonstrates superiority in capturing long-term temporal relationships. This study finds that the LSTM model consistently outperforms ARIMA for beef, buffalo meat, and chicken categories, although there is a slight increase in error for the potatoes category. Model performance evaluation is conducted using metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Squared Error (RMSE). The results indicate that the LSTM model exhibits lower errors compared to ARIMA, proving its effectiveness in predicting dynamic demand patterns. With a better understanding of product availability, the company is expected to reduce operational costs, avoid losses, and enhance customer satisfaction through more efficient supply chain management. This research provides significant insights for PT. XYZ and similar industries in implementing more accurate forecasting methodologies
Navigating Healthcare's Digital Transformation : How Service Reliability and Digital Content Strategy Drive Patient Loyalty Through Trust Ikhsan Febriansyah; Munawar Munawar; Endang Ruswanti
Global Management: International Journal of Management Science and Entrepreneurship Vol. 2 No. 3 (2025): August : International Journal of Management Science and Entrepreneurship
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/globalmanagement.v2i3.301

Abstract

The digitalization of healthcare had prompted health institutions to adopt digital content marketing as an innovative strategy for service promotion and patient loyalty development, while preserving service reliability as a crucial loyalty factor. Recent empirical research had revealed inconsistent findings about the non-linear relationships among these variables, which necessitated additional investigation incorporating trust as a mediating factor. This research sought to provide empirical validation of how trust mediated the relationship between service reliability, digital content marketing, and patient loyalty within hospital settings. The study employed a cross-sectional methodology using purposive sampling with 119 outpatients from Hermina Kemayoran Hospital in Jakarta. The analytical approach utilized the three box method for descriptive analysis and Partial Least Square-Structural Equation Modeling (PLS-SEM) via Smart-PLS 4 for inferential analysis. Results demonstrated that both digital content marketing and service reliability influenced patient loyalty indirectly through trust mediation, though digital content marketing's indirect influence was considerably weaker than its direct impact. The research determined that trust served as a partial mediator in how digital content marketing and service reliability affected patient loyalty development in hospital environments.
Identification of potential depression in social media posts Munawar, Munawar; Yulhendri, Yulhendri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2096-2103

Abstract

The widespread use of social media to convey emotions (including depression) can be used to identify suspected depression in social media posts by examining the language that they have used on social media. This study aims to develop a system for detecting suspected depression in social media posts using sentiment analysis. This study collected data from X (Twitter) for three months using the keywords depression, mental health, and mental disorders. 1,502 data were generated due to the cleaning process of the 5,000 data collected. The findings of employing the validated by psychologist valence aware dictionary and sentiment reasoner (VADER) and Indonesian sentiment (InSet) lexicons demonstrate that VADER is more accurate (95.1%) than Inset (76.9%). The results of modeling with random forest, naive Bayes, and support vector machine (SVM) showed that random forest had the highest accuracy (83.3%), followed by naive Bayes (80.5%) and SVM (80.4%). Predicting social media data using lexicons and machine learning has limits that can be addressed by validation from clinical psychology. The frequency, timing, and idiom of posts on social media can reveal signs of depression. Depression seems to be best described by words like melancholy, stress, sadness, worthlessness, and depression.
Analysis of the Implementation of Electronic Medical Records Using the HOT-Fit Method with Data Integration as an Intervening Variable at Assyifa Women and Children Hospital, Tangerang City Ryzha Ryskyanty; Munawar Munawar; Anastina Tahjoo
Quantum Wellness : Jurnal Ilmu Kesehatan Vol. 2 No. 3 (2025): September : Quantum Wellness : Jurnal Ilmu Kesehatan
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62383/quwell.v2i3.2328

Abstract

Human Organization Technology Net-Benefit (HOT-Fit) model is used to analyze information systems through 4 (four) dimensions: people, organization, technology, and net benefits. This study aims to analyze the implementation of RME with the HOT-Fit approach through data integration at RSIA Assyifa, Tangerang City. The research method is quantitative with data collection using a questionnaire distributed via Google Form. The sample consisted of 95 respondents of health workers and non-health workers who used RME. Data analysis was carried out using Structural Equation Modeling-Partial Least Square (SEM-PLS) version 3.0. In this study, there are 22 hypotheses, consisting of 17 direct influences and 5 (five) indirect influences. The results of the study show that system quality, information quality, and service quality have a significant positive effect on system use. Furthermore, system quality and information quality have a significant positive effect on user satisfaction, but service satisfaction does not affect user satisfaction. Next, system quality has a significant positive effect on organizational structure, as well as service quality which has a significant positive effect on the organizational environment. In addition, the organizational environment has a significant positive effect on net benefits, but in contrast to the variables of system usage, user satisfaction, and organizational structure which do not have a significant effect on net benefits. Related to the organizational aspect, the net benefit aspect, and data integration have a significant positive effect on RME implementation, while the technology aspect and human aspect do not have a significant effect on RME implementation. Data integration mediates the indirect effect of the technology aspect, the organization aspect, and the net benefit aspect on RME implementation, while data integration does not mediate the effect of the human aspect on RME implementation.
Rancang Bangun Aplikasi Pengelolaan Akun Zoom Meeting Di PT PLN Energi Primer Indonesia Berbasis Web Rizal Mutakin; Munawar, Munawar
Jurnal Ilmu Komputer dan Informatika | E-ISSN : 3063-9026 Vol. 2 No. 2 (2025): Oktober - Desember
Publisher : GLOBAL SCIENTS PUBLISHER

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

Abstract

Online meeting activities have become mandatory since the Covid-19 pandemic. PT PLN Energi Primer Indonesia has utilized the zoom cloud meeting application to use remote meetings for internal and external use. However, in practice, the process of ordering and creating meeting schedules is still done manually and has not been integrated so that this is felt to be less than optimal and can lead to errors caused by someone (human error) which results in clashes in the schedule for using the zoom meeting account. The purpose of this research is to design a web-based zoom meeting management application at PT PLN EPI. The system development method is Rapid Application Development (RAD). The system design uses the programming language PHP and MySQL as the database. System testing is done using black box testing. The results of this study are the successful creation of a web-based zoom meeting account management system at PT PLN EPI which is able to improve the quality of zoom meeting account management so that management related to zoom meeting scheduling can be carried out automatically because it is already integrated by the system, which can avoid clashes in the use of zoom accounts .
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 Sentimen Kalimat Depresi Pada Pengguna Twitter Dengan Naive Bayes, Support Vector Machine, Random Forest Fachriza, Mohammad; Munawar, Munawar
KOMPUTEK Vol. 7 No. 2 (2023): Oktober
Publisher : Universitas Muhammadiyah Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24269/jkt.v7i2.2218

Abstract

Pengguna aktif media sosial terus meningkat, dengan Twitter menjadi salah satu platform yang populer. Media sosial, termasuk Twitter, menjadi tempat bagi pengguna untuk mengekspresikan pendapat, termasuk mengenai masalah kesehatan mental seperti depresi. Penelitian ini bertujuan untuk menganalisis sentimen tweet pengguna Twitter terkait depresi menggunakan metode klasifikasi seperti Naïve Bayes Classifier, Support Vector Machine (SVM), dan Random Forest. Pengumpulan data tweet menggunakan metode crawling menggunakan API yang disediakan oleh Twitter dengan kata kunci yang berhubungan dengan depresi. Data tweet yang digunakan sebanyak 1502 tweet, yang selanjutnya dibersihkan pada tahap preprocessing dan diberi label dengan validasi oleh pakar terkait depresi, data yang sudah diberi label sebagai data untuk pengujian pada algoritma yang digunakan. Dari hasil pengujian performa pada algoritma yang diuji dapat disimpulkan bahwa algoritma Random Forest memilkiki hasil performa yang lebih tinggi dibandingkan dengan Naive Bayes Clasifier dan Support Vector Machine dengan Hasil  akurasi: 83.33%, presisi: 83.04%, recall: 83.33%, dan f1-scores: 82.62%. Penelitian ini Juga memberikan Word cloud untuk memberikan gambaran visual tentang kata-kata yang paling sering muncul dalam tweet. Kata-kata yang dominan dapat memberikan indikasi tentang topik atau isu yang paling mendominasi terkait kesehatan mental seseorang
The Legality of Smart Contract in the Perspectives of Indonesian Law and Islamic Law Munawar, Munawar
AL-ISTINBATH : Jurnal Hukum Islam Vol 7 No 1 May (2022)
Publisher : Institut Agama Islam Negeri Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (752.028 KB) | DOI: 10.29240/jhi.v7i1.4140

Abstract

This study aims to determine the legal status of smart contracts from the perspective of Indonesian law and Islamic law through a comprehensive literature review. The revolution of the internet and smartphones has changed human life, as happens in smart contracts. This difference in character between smart and conventional contracts has not been fully anticipated by applicable law. That is why the urgency of fiqh renewal or reform of the law in the cyberspace era. Although smart contracts are still in their infancy, and there are still many critical issues that need to be resolved, the results of the literature research show that the smart contract has fulfilled the principles in the agreement/contract in Islamic law. According to the ITE Law, a smart contract can be interpreted as an agreement referred to in Article 1313 of the Civil Code, "an act where one person binds himself to one or more other people". Although this study is not sufficient, this needs to be elaborated from the perspective of Indonesian law. The most important things in smart contracts to comply with the Islamic law are: the sequence of processes in the smart contract must comply with Islamic law, the object being transacted must be halal, the perpetrators have complied with the provisions of the Islamic law, fixed price during the contract period and the number of parties involved in the contract may increase over time.
Conceptual design for traceability and transparency in halal self-declared with blockchain Munawar, Munawar; Zulfiandri, Zulfiandri; Mugiyono, Arif
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8634

Abstract

Halal self-declared is a halal certification procedure for small micro enterprises (SMEs). However, sufficient technology support is required to ensure the halal process’s transparency and traceability. By integrating blockchain-oriented software engineering (BOSE) technology with smart contracts and electronic product code information services (EPCIS), this study aims to deliver a conceptual design for traceability and transparency in halal self-declared. The effectiveness of the blockchain in storing data and disclosing private information to interested parties can be circumvented by utilizing both off-chain and on-chain technology. The effectiveness of blockchain data storage and the ability to disclose sensitive information to interested parties can be advantageous for both on-chain and off-chain applications.
Penerapan Analisis Asosiasi Untuk Mengetahui Pola Pembicaraan Depresi Pada X Rifqi Adi Prasetya; Munawar; Habibullah Akbar; Popong Setiawati
Paradigma: Jurnal Filsafat, Sains, Teknologi, dan Sosial Budaya Vol. 31 No. 2 (2025): Paradigma: Jurnal Filsafat, Sains, Teknologi, dan Sosial Budaya
Publisher : Universitas Insan Budi Utomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33503/paradigma.v31i2.2557

Abstract

Penelitian ini bertujuan untuk mengidentifikasi pola pembicaraan yang mencerminkan gejala depresi pada media sosial X dengan menerapkan metode association rule mining. Dengan meningkatnya penggunaan media sosial sebagai wadah ekspresi emosional, studi ini berupaya mengungkap hubungan antar kata yang sering muncul bersamaan dalam konteks depresi. Penelitian ini menggunakan pendekatan Knowledge Discovery in Database (KDD) yang mencakup tahapan seleksi data, pre-processing, transformasi data, data mining, interpretasi hasil, dan validasi pakar. Data dikumpulkan melalui tools Tweet Harvest dengan kata kunci seperti “capek”, “sedih”, “stress”, “sengsara”, “lelah”, “gelisah” dan “putus asa”, menghasilkan 21.020 tweet, yang kemudian diproses dan dianalisis menggunakan algoritma Apriori dan FP-Growth. Hasilnya menunjukkan 12 aturan asosiasi yang menggambarkan ekspresi emosi negatif dengan intensitas tinggi, seperti asosiasi antara “hidup” dan “sengsara” serta “sedih” dan “banget”, yang mencerminkan fokus pada diri sendiri, kelelahan emosional, dan persepsi negatif terhadap hidup sebagai indikasi umum dari depresi. Validasi pakar mengonfirmasi bahwa pola-pola tersebut memiliki relevansi klinis. Apriori terbukti lebih efisien dari segi waktu dan penggunaan memori dibanding FP-Growth. Temuan ini menunjukkan bahwa pola bahasa di media sosial dapat menjadi indikator dini gejala depresi.
Co-Authors Akbar, Habibullah Alfa Punky Irawan Anastina Tahjoo Anugra M Arrozi, Muhammad Fachrudin Budi Tjahjono Edi Kartawijaya Endang Ruswanti Fachriza, Mohammad Gerry Firmansyah Idrus Jus'at Idrus Jus’at Ikhsan Febriansyah Iwan Ridwanudin Keishatyanarsa Kuntjoro Kusumapradja, Rokiah Marzuki Pilliang Mohammad Amada Mohammad Amada Mohammad Fachriza Mugiono, Arif Mugiyono, Arif Muhammad Abduh Nizirwan Anwar Nofierni, Nofierni Pilliang, Marzuki Rasipin Rasipin Riadi, Yosua Rian Adi Pamungkas Rifqi Adi Prasetya Rizal Mutakin Ryzha Ryskyanty Santosa, Nugroho Budhi Sejati, Puteri Setiawati, Popong Yulhendri Yulhendri Zulfiandri Zulfiandri