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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Informasi dan Teknologi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Journal of Computer Networks, Architecture and High Performance Computing Jurnal Pelita Pengabdian SAINTEK Teknologi : Jurnal Ilmiah Sistem Informasi
Effendi, Muhammad Makmun
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Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Industrial & Manufacturing Engineering

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Sentiment Analysis of Oppenheimer Movie Reviews: Naïve Bayes Algorithm for Public Opinion Noviansyah, Berliana; Effendi, Muhammad Makmun; Achmad, Yudianto
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 3 (2024): Articles Research Volume 6 Issue 3, July 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i3.4393

Abstract

The development of information and communication technology has revolutionized the way people consume and engage with media, particularly in the realm of film. Online platforms such as Netflix, Amazon Prime Video, and YouTube have transformed movie consumption habits, providing a vast array of options for viewers to explore and enjoy. A crucial aspect of this digital landscape is the proliferation of movie reviews, which serve as valuable guides for users seeking to discover films aligned with their preferences. However, the abundance of reviews, often varying in quality and objectivity, necessitates tools capable of effectively processing and understanding these textual data. This research delves into sentiment classification of Oppenheimer movie reviews, utilizing the Naive Bayes algorithm to categorize reviews into positive, negative, and neutral sentiments. The dataset comprising audience reviews and numerical ratings undergoes preprocessing using the TF-IDF method to facilitate numerical representation. Subsequently, the Naïve Bayes algorithm is trained on this processed data to accurately classify sentiments. The model demonstrates exceptional performance, achieving an accuracy rate of 97.45% in distinguishing between positive, negative, and neutral sentiments within Oppenheimer movie reviews. This study underscores the efficacy of the Naive Bayes algorithm in sentiment classification and emphasizes the significance of employing techniques like TF-IDF for enhancing sentiment analysis in the domain of movie reviews.
Analysis Prediksi Wilayah Rawan Banjir dengan Algoritma K-Means Effendi, Muhammad Makmun; Inka, Inka; Siswandi, Arif
Journal of Information System Research (JOSH) Vol 5 No 2 (2024): Januari 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i2.4770

Abstract

Along with the high amount of rainfall in Bekasi -West Java, floods have started to inundate several areas of Bekasi , one of the causes is the high rainfall factor. According to (Regional Disaster Management Agency) BPBD, the most flood points are in the Bekasi area, causing several activities of the surrounding community to be disrupted, transportation hampered, and also the emergence of disease problems such as skin diseases, diarrhea, and so on. The problem of flooding is a shared responsibility that requires a solution. also the role of technology to help facilitate the provision of information to the public regarding flood-prone areas in the Bekasi area. One technique that can be used is using the K-Means Clustering Algorithm to group flood-prone areas. The flood dataset was processed using the RapidMiner application, for the dataset taken to carry out this analysis from January to December 2022, there were 24 data from areas affected by flooding from various sub-districts and villages in the city of Bekasi. The results of the research produced 3 clusters, namely, the high flood, medium flood and low flood categories, which received a Davies Bouldin index value of -0.452.
Integrasi Programmable Logic Control Outseal Mega V.2 dengan NodeMCU ESP826 dengan menerapkan Internet of Things Effendi, Muhammad Makmun; Taufiq Khasanah; Nurhadi Sirojudin
TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Vol 13 No 1 (2023): January
Publisher : Universitas Pesantren Tinggi Darul 'Ulum (Unipdu) Jombang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/teknologi.v13i1.3868

Abstract

Kemajuan dan Perkembangan Teknologi yang sangat pesat, sehingga di dalam dunia industri memerlukan alat bantu yang lebih modern dan bekerja secara otomatisasi.   Pada saat ini dunia industri terutama mesin injection dan mesin press stamping produksi sudah menggunakan PLC namun belum bekerja secara otomatisasi dan belum terintegrasi dengan Internet of Thing’s. Untuk mempermudah aktivitas produksi maka penulis membuat hardware untuk mesin industri otomatisasi berbasis PLC Outseal Mega V.2 sebagai alternatif mesin industri tersebut dengan metode pendekatan kualitatif. Hardware yang dibuat dengan menggunakan NodeMCU ESP8266 dan modbus RTU serial RS485 dan Arduino Nano dan Blynk diinstalasi secara terstruktur dengan mekanisme adalah NodeMCU ESP8266 terintegrasi dengan modbus RTU serial RS485, sedangkan PLC Outseal Mega V.2 Slim, Arduino Nano terhubung dan terintegrasi dengan modbus RTU serial RS485 dan untuk Blynk NodeMCU ESP8266 terintegrasi dengan wifi. Setelah dirangkai selanjutnya dilakukan pengujian dengan jarak 15 meter PLC outseal Mega V.2 dapat dikontrol Blynknya berbasis Android sehingga integrasi PLC outseal Mega V.2 dapat digunakan dan bekerja di mesin injection maupu mesin pres stamping dapat bekerja secara otomatisasi.
Implementasi Teknologi AI Implementasi Teknologi AI untuk Efisiensi Penyusunan Modul Ajar pada Guru MI Unggulan Al Kahfi : Penyusunan Modul Ajar Effendi, Muhammad Makmun
Jurnal Pelita Pengabdian Vol. 4 No. 1 (2026): Januari
Publisher : DPPM Universitas Pelita Bangsa

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

Abstract

Penyusunan modul ajar merupakan salah satu tugas esensial guru Madrasah Ibtidaiyah (MI) dalam mendukung pelaksanaan pembelajaran yang efektif dan sesuai dengan Kurikulum Merdeka. Namun, pada praktiknya, guru MI masih menghadapi berbagai kendala, seperti keterbatasan literasi digital, tingginya beban administratif, kesenjangan kompetensi, serta terbatasnya akses terhadap sumber belajar yang mutakhir. Kondisi tersebut berdampak pada rendahnya efisiensi dan inovasi dalam penyusunan modul ajar. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk mengimplementasikan teknologi Artificial Intelligence (AI) sebagai solusi strategis dalam meningkatkan efisiensi dan kualitas penyusunan modul ajar pada guru MI Unggulan Al Kahfi. Metode pelaksanaan kegiatan meliputi pelatihan, pendampingan, dan praktik langsung pemanfaatan aplikasi AI dalam penyusunan modul ajar yang terstruktur, adaptif, dan kontekstual. Hasil yang diharapkan dari kegiatan ini adalah meningkatnya literasi digital guru, berkurangnya beban administratif, serta terwujudnya modul ajar yang lebih inovatif dan relevan dengan kebutuhan peserta didik. Selain memberikan manfaat langsung bagi guru dan lembaga mitra, kegiatan ini juga mendukung implementasi Merdeka Belajar Kampus Merdeka (MBKM), pencapaian Indikator Kinerja Utama (IKU) perguruan tinggi, serta fokus pengabdian masyarakat di bidang pendidikan dan transformasi digital. Dengan demikian, pemanfaatan teknologi AI diharapkan mampu berkontribusi secara berkelanjutan terhadap peningkatan mutu pendidikan dasar Islam.
Precision Medicine Through Support Vector Machines Analyzing Patient Data for Improved Drug Classification Anwar, Nanda Rosma; Pramudito, Dendy K; Effendi, Muhammad Makmun
Jurnal Informasi dan Teknologi 2025, Vol. 7, No. 2
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60083/jidt.vi0.627

Abstract

Selecting the appropriate medication is crucial for ensuring optimal therapeutic outcomes and minimizing adverse effects for patients. Healthcare personnel are managing an increasing volume of medical data in the digital era. Identifying swift, precise, and dependable methods for recommending appropriate medications is becoming essential. This study aims to meet this criterion by classifying drugs into appropriate categories for patient care using the Support Vector Machine (SVM) technology. The research utilized a dataset from GitHub comprising 200 patient records. These records furnish critical information regarding the patient, including age, sex, blood pressure, cholesterol levels, sodium-to-potassium ratios, and prescriptions. To maximize the use of this data, the method entails several critical steps: selecting appropriate data, meticulously cleaning and organizing it, transforming it for analytical readiness, employing SVM for data mining, and conducting a comprehensive review. The dataset is divided into two segments which are 20% is allocated for testing the efficacy of the SVM model, while the remaining 80% is designated for training the model.The primary tool for constructing the SVM model is the Google Colaboratory platform, which utilizes Python. A confusion matrix is employed to meticulously evaluate the performance of a model. It provides valuable metrics such as accuracy, precision, recall, and the F1 score. The evaluation method indicates that the SVM model holds significant potential for systematically assessing patient data due to its capability to appropriately categorize various drug types. This discovery represents a significant advancement for AI in healthcare, as it facilitates the prompt and straightforward recommendation of individualized medicines by physicians.
Co-Authors . Ermanto Achmad, Yudianto Anggi Muhammad Rifa'i Anwar, Nanda Rosma Arwan, Asep Dendy K. Pramudito Inka, Inka Isarianto, Isarianto Noviansyah, Berliana Nurhadi Sirojudin Purnama Sakhrial Purnama Sakhrial Pradini Sen , Tjong Wan Siswandi, Arif Surojudin, Nurhadi Taufiq Khasanah Yunus, Imsal Zy, Ahmad Turmudi