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All Journal Jurnal Pengabdian Mandiri Jurnal Penelitian Ilmu komputer APPA : Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmu Komputer
AGUNG BUDI SUSANTO
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Social Sciences Other

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DESAIN DAN PENGEMBANGAN INTERNET OF THINGS SEBAGAI MONITORING KETERSEDIAAN AIR BERSIH BERBASIS NodeMCU ESP8266 (STUDI KASUS: KP.BANTARWARU KECAMATAN MUNCANG KABUPATEN LEBAK) Dede Lukman Lukman Hakim; Agung Budi Susanto; Sudarno Wiharjo
JUPIK : Jurnal Penelitian Ilmu komputer Vol. 1 No. 3 (2023): September
Publisher : PT Triputra Sejahtera Prima

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Abstract

One of the essential human needs that must be fulfilled is clean water, and using reservoirs is one of the alternative to collect clean water that from mountains. The process of distributing water from the mountain to the reservoirs sometimes encounter issues that chough up the water from reaching the reservoirs. These issues can be caused by natural phenomena, such as a decrease in the quality of the water source, or other factors like leaks or pipes disconnection. Periodic monitoring of the water source's condition is necessary to identify the causes of these problems. One of the method for monitoring the condition of mountain water sources is by utilizing the concept of the Internet of Things (IoT). This allows remote monitoring using water flow sensor to collect data on water flow and ultrasonic sensor to measure water levels. The data is then transmitted via the internet using the NodeMCU 8266 microcontroller. The research stages include identifying needs, needs and design analysis, variable value analysis, program design, program development, and testing. The testing involves functional and performance tests to ensure the system operates effectively, enabling personnel to identify the causes of issues that cough up water from reaching the reservoirs. The result of this research is a device that can monitor the availability of water in the reservoirs and easily track the quantity of clean water from the water source through a website.
INNOVATIVE WORKSHOP OF MASTERY REMOTE DESKTOP AND TEAMVIEWER FOR STUDENTS OF DHARMA VOCATIONAL SCHOOL KARAWACI OF COMPUTER NETWORK DEPARTMENT Taswanda Taryo; Achmad Hindasyah; Agung Budi Susanto; Farkhan Mubarok; Nining Suharwati; Dahlan Supriatna; Suharyadi; Gregorius Eduard D. P.; Entis Sutisna; Mukhlishoh Syaukati Robbi; Ajeng Permata Suri
JURNAL PENGABDIAN MANDIRI Vol. 4 No. 1: Januari 2025 (In Press)
Publisher : Bajang Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53625/jpm.v4i1.9581

Abstract

Information and communication technology (ICT) has become the backbone of various industrial sectors. There are many applications that can be offered to solve computer repair problems remotely and Remote Desktop and TeamViewer applications are two of them. Dharma Siswa Karawaci Vocational School is committed to preparing graduates who are ready to work and highly competitive in the industrial world. The PKM of Postgraduate Program in Informatics Engineering, Pamulang University-UNPAM conducted an innovative workshop on Remote Desktop and TeamViewer on October 19, 2024. A survey showed they are satisified to participate in the workshop. Student participants can improve the skills of the Vocational School students in mastering Remote Desktop and TeamViewer technology for solving problems and managing networks remotely. The workshop will provide practical experience that suits the current needs of the IT industry, through direct training and simulations. Considering that the workshop only takes a few hours, the student participants of the workshop can continue their training not only at the school or at home, but also can communicate with the Team of S2 TI UNPAM. If necessary, the follow-up workshops can be carried out either offline or online.
ANALISIS DATA UNTUK MENENTUKAN TARGET PEMASARAN YANG TEPAT DAN MENINGKATKAN PEMBELAJARAN SISWA DENGAN MENGGUNAKAN ORANGE DATA MINING M. M. Rizki Reza Utama; Mokhamad Yusron Rafi; Faizal Alfiana Sakti; Risko; Suryatna Sacadibrata; Nia Adiyanti; Dessi Eka Sastie; Agung Budi Susanto
APPA : Jurnal Pengabdian Kepada Masyarakat Vol 3 No 1 (2025): APPA : Jurnal Pengabdian kepada Masyarakat
Publisher : Shofanah Media Berkah

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Abstract

Kegiatan Pengabdian kepada Masyarakat (PkM) ini bertujuan untuk meningkatkan literasi data di kalangan siswa SMK melalui pelatihan penggunaan Orange Data Mining, sebuah perangkat lunak open-source yang memungkinkan analisis data secara visual dan interaktif. Kegiatan ini dilaksanakan di SMK Sasmita Jaya 1, Pamulang, dengan melibatkan siswa dari jurusan Teknik Komputer dan Jaringan. Pelatihan difokuskan pada dua aspek utama, yaitu pemanfaatan data mining untuk strategi pemasaran yang lebih tepat sasaran dan untuk mendukung proses pembelajaran berbasis data. Metode pelaksanaan meliputi sosialisasi konsep dasar data mining, pengenalan antarmuka Orange, serta praktik langsung dalam mengolah dan menganalisis dataset. Materi yang disampaikan mencakup teknik klasifikasi, klasterisasi, dan visualisasi data, dengan studi kasus terkait perilaku pelanggan dan analisis pola belajar siswa. Hasil kegiatan menunjukkan bahwa peserta mampu memahami dan mengimplementasikan konsep dasar analisis data dengan baik. Mereka juga menunjukkan peningkatan minat terhadap teknologi data dan pemahaman mengenai penerapannya dalam kehidupan nyata, baik di bidang bisnis maupun pendidikan. Kegiatan ini tidak hanya memberikan keterampilan teknis, tetapi juga mendorong pola pikir berbasis data (data-driven mindset) sejak dini.
Analisis Data Produksi Biskuit Dengan Algoritma Naive Bayes Dan Random Forest Sabarrudin; Agung Budi Susanto; Sajarwo Anggai
Jurnal Ilmu Komputer Vol 2 No 2 (2024): Jurnal Ilmu Komputer (Edisi Desember 2024)
Publisher : Universitas Pamulang

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Abstract

In the manufacturing industry, production problems often occur, often production does not match market demand, production is not well planned, therefore this study aims to develop a classification model using machine learning based on the Naive Bayes and Random Forest algorithms to classify biscuit production data. The main focus of this study is to utilize variables such as dough, number of mixers, production time parameters, and other relevant production factors to improve accuracy in classification. The dataset used in this study includes information from several previous production periods, namely data in 2019-2023, which is then used to train and test the Naive Bayes and Random Forest algorithm models. The training and validation process is carried out using commonly used model performance evaluation techniques. The results of the study show that the Random Forest model is able to provide high accuracy, namely 97.54% while Naive Bayes is 96.45%. Further analysis was also carried out to identify the variables that most influence production results, providing additional insights for optimizing the production process. The results of this study can contribute to the development of classification models for the food and beverage industry, especially in biscuit products, but also offer a more specific view of the factors that influence biscuit production. The implementation of this study can be a basis for manufacturers to make more precise and effective decisions in managing their production.
Evaluasi Efektivitas Tata Kelola Teknologi Informasi Di Rumah Sakit Umum Daerah Provinsi Nusa Tenggara Barat Dengan Menggunakan Kerangka Kerja Cobit 2019 Muh. Yusril Hidayat; Agung Budi Susanto
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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Abstract

Hospitals have a strategic responsibility to improve the quality of public health services. However, information technology (IT) management in hospitals often faces challenges such as lack of long-term planning, which causes information to be inefficient and ineffective. This study aims to evaluate the effectiveness of information technology governance at the West Nusa Tenggara Provincial Hospital using the COBIT 2019 framework. The focus of the study includes risk management (APO 12), change management (BAI 06), and security service management (DSS 05). The method used is a case study with a qualitative approach, involving interviews and questionnaires for data collection. The evaluation results show that the current average capability level is 2.5 with a target of 3. Key findings include the need for improvement in risk management and security services. Recommendations for improvement include the development of new risk policies, staff training, and adoption of the latest security technology. Implementation of COBIT 2019-based suggestions is expected to improve the quality of services and performance of information technology at the NTB Provincial Hospital.
Analisis Sentimen Terhadap Istana Garuda Di Ibukota Nusantara (IKN) Menggunakan Algoritma Random Forest Dan Support Vektor Machine Jihansyah, Muhamad; Agung Budi Susanto; Arya Adhyaksa Waskita
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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Abstract

ABSTRACT The relocation of Indonesia's capital city (IKN) to East Kalimantan is a national strategic project that has sparked diverse public opinions, particularly regarding the construction of Garuda Palace. This study aims to analyze public sentiment toward the Garuda Palace project using Random Forest and Support Vector Machine (SVM) algorithms and to compare their performance based on accuracy, precision, recall, and F1-score. This research offers three key novelties. First, it focuses on public opinion regarding the Garuda Palace project at IKN, which is underexplored in both local and international literature. Second, the use of Inset and Senti labeling techniques introduces a novel approach to sentiment categorization. Third, the comprehensive evaluation of Random Forest and SVM performance provides new insights into their effectiveness in large-scale infrastructure sentiment analysis in Indonesia. The methodology consists of five stages: (1) Data collection through web scraping from Twitter (July-August 2024) using keywords related to "Garuda Palace" and "IKN"; (2) Data preprocessing, including tokenization, stopword removal, stemming, and TF-IDF transformation; (3) Data labeling using Inset and Senti approaches; (4) Model training with Random Forest and SVM algorithms; (5) Model evaluation using confusion matrices and performance metrics such as accuracy, precision, recall, and F1-score. Results indicate that Random Forest achieved 77% (Inset) and 89% (Senti) accuracy, excelling in detecting negative sentiment with an F1-score of 0.93 on the Senti dataset. SVM achieved 89% (Inset) and 91% (Senti) accuracy, performing better in detecting positive sentiment with a precision of 0.96 on the Senti dataset. This study provides valuable insights into public perceptions of national infrastructure projects, supports data-driven decision-making, and serves as a reference for future sentiment analysis systems
Analisis Topik Penelitian Pendidikan Matematika Di Indonesia Dengan Menggunakan Metode Latent Dirichlet Allocation (LDA) junedi, Beni; Agung Budi Susanto; Sajarwo Anggai
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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Abstract

On the research topic of Mathematics Education readers or researchers still have difficulty identifying research topics in the field of Mathematics Education. This is because there is no system or model that can be seen or used in determining research topics. Besides that, there is no automation of the research direction of Mathematics Education in Indonesia using topic modeling, so it is necessary to conduct a study or research on this. In research, the most important thing is the trend of research that is currently developing so that it can determine the novelty of the studies that have been done before. While there is no system used to determine trends and state of the art from research in the field of Mathematics Education. The aim of the research is to find out an overview of the research topics in Mathematics Education in Indonesia in 2020-2023 and to find out the implementation of modeling research topics in Mathematics Education in Indonesia using the Latent Dirichlet Allocation (LDA) method for 2020-2023. The research design consisted of literature study, data collection, data pre-processing: tokenization, case folding, stopword removal, and stemming, topic analysis with LDA, evaluation of the LDA method, and conclusions. Analysis of Topic Modeling with Latent Dirichlet Allocation using packages used from python including the Gensim and pyLDAvis packages. Based on the coherence score, the best number of topics (K) = 18, with a coherence score = 0.426 (the highest), it can be concluded that the number of topics produced is 18 topics.
Analisis Eksperimental Kinerja Transformers, VADER, dan Naive Bayes dalam Analisis Sentimen Teks Bahasa Indonesia: Studi Kasus Komentar Terkait Judi Online Sugiyo; Agung Budi Susanto; Sajarwo Anggai
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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Abstract

Sentiment analysis is a subfield of Natural Language Processing (NLP) that focuses on detecting and classifying opinions expressed in textual data. In the digital social context, the increasing volume of public comments related to online gambling in Indonesia highlights the need to map public perception. This study aims to conduct an experimental analysis of the performance of three popular sentiment analysis approaches: VADER (Valence Aware Dictionary and sEntiment Reasoner), Naive Bayes, and Transformers-based models, specifically on Indonesian-language text. The dataset consists of public comments from social media and digital platforms containing keywords related to online gambling. The research process involves text preprocessing, data labeling, model training (for Naive Bayes and Transformers), and performance testing. Evaluation metrics include accuracy, precision, recall, and F1-score. The experimental results show that the Transformers model (using IndoBERT) achieves the highest performance in terms of accuracy and generalization ability, while VADER performs less optimally due to its limitations in understanding Indonesian linguistic context. Naive Bayes demonstrates moderate and consistent performance but lacks the capability to capture complex contextual meanings. These findings contribute to selecting appropriate sentiment analysis methods for non-English languages and support the development of more accurate public opinion detection systems in the future
Analisis Tipe Kecerdasan Majemuk Siswa Sekolah Dasar Berbasis Catatan Perilaku Menggunakan Algoritma Naive Bayes, K-Nearest Neighbors, dan Support Vector Machine Nursalam, Asep Herman; Agung Budi Susanto; Taswanda Taryo
Jurnal Ilmu Komputer Vol 3 No 1 (2025): Jurnal Ilmu Komputer (Edisi Juli 2025)
Publisher : Universitas Pamulang

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This study aims to identify the types of multiple intelligences of elementary school students based on Howard Gardner's theory by utilizing machine learning algorithms, namely Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). The data used comes from student behavior records and intelligence type questionnaires obtained from students or parents. The SEMMA method (Sample, Explore, Modify, Model, Assess) is used, including text preprocessing and TF-IDF feature extraction. The classification process is carried out using Orange Data Mining software and evaluated using accuracy, precision, recall, F1-score, and AUC metrics. The evaluation results show that the SVM algorithm provides the best performance with an accuracy of 93.30% and AUC of 0.997. Naive Bayes follows with 90.50% accuracy and 0.994 AUC, while KNN reaches 89.50% accuracy and 0.941 AUC. The study also results in a web-based application prototype that classifies students' intelligence types and provides personalized learning recommendations. This confirms the effectiveness of machine learning in supporting personalized learning and student potential development.
Co-Authors Achmad Hindasyah Ajeng Permata Suri Anggai, Sajarwo Arya Adhyaksa Waskita Dahlan Supriatna Dede Lukman Lukman Hakim Dessi Eka Sastie Entis Sutisna Faizal Alfiana Sakti Farkhan Mubarok Gregorius Eduard D. P. Jihansyah, Muhamad Junedi, Beni M. M. Rizki Reza Utama Mokhamad Yusron Rafi Muh. Yusril Hidayat Mukhlishoh Syaukati Robbi Nia Adiyanti Nining Suharwati Nursalam, Asep Herman R. Suharyadi Risko Sabarrudin Sudarno Wiharjo Sugiyo Suryatna Sacadibrata Taswanda Taryo