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All Journal JURNAL SISTEM INFORMASI BISNIS MediaTor: Jurnal Komunikasi Jurnal Simetris Mediator Bulletin of Electrical Engineering and Informatics Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Pembangunan Pedesaan Journal of Information Systems Engineering and Business Intelligence Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Register: Jurnal Ilmiah Teknologi Sistem Informasi KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Science and Technology Indonesia JOIV : International Journal on Informatics Visualization INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Komputasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING ComTech: Computer, Mathematics and Engineering Applications Jurnal Pengabdian Kepada Masyarakat (JPKM) Tabikpun Buguh: Jurnal Pengabdian Kepada Masyarakat International Journal of Electronics and Communications Systems Jurnal Pepadun SWARNA: Jurnal Pengabdian Kepada Masyarakat JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Integra: Journal of Integrated Mathematics and Computer Science Jurnal Komputasi
Akmal Junaidi
Jurusan Ilmu Komputer Universitas Lampung
Religion Arts Humanities 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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Journal : integra journal of integrated mathematics and computer science

 1 
The Expert System for Diagnosing Pest and Disease in Pineapple Plant Using the Iterative Deepening Search (IDS) Method on the Android Platform Amalia, Ayu; Junaidi, Akmal; Sudarsono, Hamim; Lumbanraja, Favorisen R.
Integra: Journal of Integrated Mathematics and Computer Science Vol. 1 No. 1 (2024): March
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.2024119

Abstract

This research was conducted to design and develop pineapple pests and diseases diagnosis expert system with Iterative Deepening Search (IDS). This expert system runs on android platform. The certainty factor of this expert system is initialized by an expert and the final certainty factor is computed by the system. The data used in this expert system consist of 5 types of pineapple pests, 6 types of pineapple diseases. 31 types of symptoms and 11 types of rules are used to diagnose pineapple pests and diseases. To validate this expert system, two types of tests were conducted, which are expert system verification and system evaluation by users. Expert system verification was conducted by comparing 10 results from the diagnosis system and the results of the diagnosis by an expert. The compare result shows that the expert system result 100% is similar to the result of the expert. To evaluate the system, 30 respondents were asked to evaluate using questionnaires, which were grouped into three groups, i.e. group I (pineapple experts), group II (pineapple farmers and agriculture students) and group III (computer science students). All three stated this expert system runs well (75.56%, 72.44%, and 79.83% respectively).
Comparison of Support Vector Regression and Random Forest Regression Performance in Vehicle Fuel Consumption Prediction Nurdin, Muhaymi; Wamiliana; Junaidi, Akmal; Lumbanraja, Favorisen Rossyking
Integra: Journal of Integrated Mathematics and Computer Science Vol. 1 No. 2 (2024): July
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20241221

Abstract

Predicting vehicle fuel consumption is an important aspect in improving energy efficiency and supporting sustainable transportation. This study aims to compare the performance of Support Vector Regression (SVR) and Random Forest Regression (RFR) algorithms in predicting combined vehicle fuel consumption (COMBINED, a combination of 55% urban and 45% highway). The Canadian government's Fuel Consumption Ratings dataset was used, with 2015-2023 data (9,185 entries) for training and testing, and 2024 data (764 entries) for further testing. Pre-processing involved StandardScaler for numerical features and OneHotEncoder for categorical features, followed by hyperparameter optimization using Grid Search, resulting in optimal parameters: SVR (C=100, epsilon=0.5, gamma=1) and RFR (n_estimators=200, max_depth=None, min_samples_split=2). Results show RFR is superior with R2 0.8845, RMSE 0.9671, and MAE 0.6566, compared to SVR with R2 0.8648, RMSE 1.0462, and MAE 0.7150. Evaluation on 2024 data and visualization of error distribution corroborate the superiority of RFR. This study concludes RFR is more effective for COMBINED prediction, although SVR is competitive post-optimization, and contributes to the selection of machine learning models for green vehicle technology.
Traffic Violation Modeling Using K-Means Clustering Method: A Case Study in Bandung, Indonesia Junaidi, Akmal; Manurung, Yunita Rosalina; Shofiana, Dewi Asiah; Lumbanraja, Favorisen Rosyking
Integra: Journal of Integrated Mathematics and Computer Science Vol. 1 No. 3 (2024): November
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20241326

Abstract

Violations of traffic regulations are both an issue and a problem that persists as a feature of life, especially in metropolitan regions such as Bandung. Traffic violation has both behavioral and environmental patterns, with different types of violations occurring at different times during the day. This negligence stems largely from not properly equipping the vehicle with the necessary documents, especially for drivers who do not pay attention to proper document preparation. With the goal of increasing road safety, law enforcement bodies face the ongoing challenge of managing rising traffic violation rates which results in a growing backlog of violation cases and a corresponding backlog workload for police departments. Comprehensive preventive strategies for the problem are extremely difficult to implement in the absence of streamlined mechanisms for the efficient allocation of limited police resources. Currently, agencies responsible for managing violation records are still using a manual desktop system based on Microsoft Excel spreadsheets. This method impedes the analysis of large datasets to derive actionable insights that could inform targeted, data-driven strategies needed to guide proactive measures. In this regard, this study attempts to implement the K-Means clustering technique in order to identify and classify high-incidence traffic violation areas in Bandung. Using this technique, the research classifies the city into three violation risk clusters: very prone, prone, and moderately prone areas. The map of the classes demonstrates the distribution of these clusters spatially, illustrating clearly and vividly how stakeholders can visualise the pattern of traffic violations. This method improves the understanding of data and at the same time boosts purposeful planning for the safety and public traffic order anticipations.
IoT-Based Air Conditioner Monitoring and Control at PT XYZ: A Prototype Approach Utilizing Node MCU ESP8266, Relay Modules, and DHT11 Temperature Sensors Innaya, Thalia Gemi; Junaidi, Akmal; Alfikri, Fadli; Kurniawan, Didik; Iqbal, Muhammad
Integra: Journal of Integrated Mathematics and Computer Science Vol. 2 No. 2 (2025): July
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20252237

Abstract

This study presents the development and implementation of an IoT-based monitoring and control system for air conditioners (AC) at PT XYZ. Leveraging the capabilities of Node MCU ESP8266, relay modules, and DHT11 temperature sensors, the proposed system enables real-time monitoring of AC status and room temperature. It also facilitates control over individual and multiple AC units, thereby enhancing operational efficiency. The system’s design focuses on energy efficiency, aiming to reduce unnecessary power consumption by ensuring that AC units are active only when needed. The system includes features for monitoring the AC’s on/off status and providing historical temperature data through graphical charts. This real-time data display allows users to track temperature trends and make informed decisions regarding AC operation. The prototype approach employed in this research involves several stages: communication with stakeholders, planning, modeling, prototype development, deployment, and iterative feedback. Testing results indicate that the system effectively meets the research objectives by providing accurate temperature readings and reliable AC control. Compared to existing solutions, this system offers enhanced functionality and integration, contributing to energy savings and improved management of AC units. This research contributes to the field by addressing gaps identified in previous studies and demonstrating the practical application of IoT technology in energy management for air conditioning systems.
Color-Based Spot Detection Using Automatic Leaf Segmentation in Potato Plants Sholehurrohman, Ridho; Sari, Kartika; Junaidi, Akmal
Integra: Journal of Integrated Mathematics and Computer Science Vol. 2 No. 3 (2025): November
Publisher : Magister Program of Mathematics, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/integrajimcs.20252339

Abstract

Potato (Solanum tuberosum L.) is one of the world’s major food crops, playing a vital role in supporting food security and nutritional resilience. However, its productivity is often threatened by foliar diseases such as early blight and late blight, which can cause significant yield losses. This study aims to develop a lightweight and explainable classification method for detecting potato leaf diseases based on automatic leaf segmentation and color-based spot analysis. Early and accurate disease detection is essential to support preventive actions in plant protection. The proposed method integrates automatic leaf segmentation using HSV-based thresholding to isolate the leaf region from the background, followed by color-based spot detection to identify disease symptoms. Extracted features include spot area, number of detected spots, and average hue values, which were then classified into three categories (healthy, early blight, and late blight) using a rule-based approach. Validation was conducted by manually comparing classification outputs with ground truth derived from file names. The results show that the method can successfully segment potato leaves, detect spot regions, and classify disease types consistently with manual validation. Although not evaluated through large-scale statistical metrics, the findings indicate that this color-based approach provides a reliable foundation for lightweight potato leaf disease detection without requiring deep learning models.
Co-Authors - Damayanti . Wamiliana Admi Syarif Ahmad Ari Aldino Ahmad Faisol Akbar, Mohammed Raihan Albertus Sudirman Alfikri, Fadli Andrian, Rico Ani Kurniawati Arif Munandar Arif Pebriansyah Aristoteles Asmiati Asmiati Ayu Amalia Ayu Nadila Bambang Hermanto Beni Adi Pranata Damayanti Damayanti Dewi, Tasya Nursita Dwi Sakethi Dwi Sakethi Dwi Sakethi Edi Arif Effendi Fajriyanto Fajriyanto Farida Ariyani Fazri, Yudistira Febi Eka Febriansyah Fitriani Gamal, Mohammad Danil Hendry Hamim Sudarsono . Heningtyas, Yunda Herindri Samodera Utami, Bernadhita Hidayat Pujisiswanto Hijriani, Astria Ida Nurhaida1 Ida Nurhaida Ida Nurhaida Ilman, Igit Sabda Indah Mayatika Sihaloho Innaya, Thalia Gemi Irawati, Anie Rose Irwan Adi Pribadi Irwan Adi Pribadi Iva Mutiara Indah Kartika Sari Kenny Claudie Fandau Kurnia Muludi Leila Fauziah Lucia Ratnasari Lumbanraja, Favorisen R Machudor Yusman Machudor Yusman Maharani, Devi Manurung, Yunita Rosalina Megawaty, Dyah Ayu Meizano Ardhi Muhammad Muhammad Iqbal Muhammad Jamaludin Muhammad, Meizano Ardhi Mustofa Usman Naurah Nazhifah Nikken Prima Puspita Nirwana Hendrastuty Nova Ayu Lestari Siahaan Novita Dwilestari Nugroho Susanto, Gregorius Nurdin, Muhaymi Ossy Dwi Endah Wulansari Ossy Dwi Endah Wulansari, Ossy Dwi Endah Pairul Syah Pairulsyah Pairulsyah Parjito Parjito Prabowo, Rizky Pranata, Beni Adi Putri, Gendis Ananda Rahmat Safe'i Rahmi Permata Hati Rangga Agustiantino Rangga Firdaus Renada Dhea Armelia Rendi Adam Rendi Eko Prasatiawan Reni Permata Sari Reza Aji Saputra Reza Habibi, Mohammad Rizky Ramadhany Rosdiana, Siti Rusdi Evizal S Susiyani Saiful Anwar Saipul Anwar Saipul Anwar Samsul Bakri Shofiana, Dewi Asiah Sholehurrohman, Ridho Siti Khabibah Siti Rosdiana Slamet Budi Yuwono Sugaluh Yulianti Sugeng P Hariyanto Sugeng P Hariyanto Sugeng Prayitno Hariyanto Susanto, Gregorius Nugroho Sutyarso Sutyarso Sutyarso, - Syachrul Priyo Wibowo Syifa Trianingsih Taqwan Thamrin Tio Arisandi Titik Nur Aeny Trianingsih, Syifa Tristiyanto Tsani, Machya Kartika Wamiliana Warsono Warsono Wartariyus Wartariyus Wiwin Susanty Wulan Kurnia Safitri Yoannisa Egeustin YOHANA TRI UTAMI, YOHANA TRI Yusikania Dwi Putri Zaenal Abidin Zaenal Abidin Zaini, TM