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All Journal Bulletin of Electrical Engineering and Informatics Cyberspace: Jurnal Pendidikan Teknologi Informasi Jurnal Teknik Informatika (JUTIF) Heca Journal of Applied Sciences J-SIGN (Journal Of Informatics, Information System, And Artificial Intelligence) Leuser Journal of Environmental Studies Journal of Educational Management and Learning Infolitika Journal of Data Science PESARE: Science and Engineering Service Journal
Irvanizam, Irvanizam
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Public Health Other

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Sistem Penyewaan Ruang dan Fasilitas Gedung Academic Activity Center (AAC) Dayan Dawood berbasis Web Kurniawaty, Risma; Irvanizam, Irvanizam; Saputra, Kurnia
J-SIGN (Journal of Informatics, Information System, and Artificial Intelligence) Vol 1, No 01 (2023): May
Publisher : Department of Informatics, Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/j-sign.v1i01.31794

Abstract

Syiah Kuala University has the Dayan Dawood Academic Activity Center (AAC) Building as a non-academic business unit that focuses on renting rooms and facilities for various purposes. This building is very often used for various activities, but unfortunately, the rental procedure is still conventional so it is prone to errors and loss of reservation data so the income calculation is not optimal. In addition, price and facility information is not yet accessible to the public. The problem will be solved by creating a website-based information system that can manage the rental of the Dayan Dawood Academic Activity Center (AAC) building with two groups of users, namely admins and tenants. This system was built using the CodeIgniter framework and MYSQL database. The system creation process applies the Scrum method as project management. System testing consists of functionality testing using Black Box Testing and Usability Testing using UMUX-LITE. Black Box Testing has successfully run all test scenarios as they should. Usability Testing was conducted on 29 respondents and resulted in a score of 83.97, the application is in the "A" category so it can be concluded that the application can be accepted with a grade scale of "B" and its adjective rating is "Good".
Rainfall forecasting by utilizing adaptive neuro-fuzzy inference system in Aceh Besar District Sofyan, Hizir; Tatsara, Nidya; Yolanda, Yolanda; Usman, Tarmizi; Irvanizam, Irvanizam
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.8441

Abstract

Forecasting is a common thing to capture events in future based on previous information. However, some classical time-series methods, including moving average (MA), autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), and simple exponential smoothing (SES), have limitations in predicting nonlinear time-series data. Therefore, this paper aims to utilize the adaptive neuro-fuzzy inference system (ANFIS) model, a combination of the fuzzy inference system (FIS) and neural network architecture to forecast a nonlinear rainfall problem. This model can capture the non-linear data, adaptation capability, and speedy learning capacity. We used the data consisting of temperature (ºC), humidity (%), and wind speed (km/hour) as input variables and rainfall (millimeter) as an output variable at two stations and one rain post in Aceh Besar District, from January 2009 to December 2019. The results demonstrated that ANFIS with generalized Bell (gBell) membership function on epoch 10 can successfully conduct rainfall forecasting in Aceh Besar District with the best-predicted value. The mean absolute percentage error (MAPE) of the prediction at the Meteorology, Climatology, and Geophysics Agency (MCGA) Station or Badan Meteorologi, Klimatologi dan Geofisika (BMKG) Indrapuri is 6.73% for 80% of the training dataset and 20% of the testing dataset.
Penerapan Aplikasi-Aplikasi Microsoft Office dan Google Docs dalam Upaya Peningkatan Media Pembelajaran di Madrasah Aliyah Negeri 5 Bireuen Irvanizam, Irvanizam; Misbullah, Alim; Zulfan, Zulfan; Farsiah, Laina; Subianto, Muhammad
PESARE: Jurnal Pengabdian Sains dan Rekayasa Vol 1, No 1 (2023): Oktober 2023
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24815/pesare.v1i1.33833

Abstract

This community service activity aims to introduce Microsoft Office and Google Docs applications to teachers and students at Madrasah Aliyah Negeri (MAN) 5 Bireuen as an online teaching media in performing teaching and learning processes during the COVID-19 pandemic. This activity was carried out by a community service team of lecturers from the Department of Informatics, Universitas Syiah Kuala. The activity was held for three days from 2 until 4 April 2021 and consisted of two sessions. The first session introduced Microsoft Office applications for learning at the high school level. The second session demonstrates Google Docs applications for providing teaching materials. The activity participants were very enthusiastic about participating in this activity by asking lots of questions and being explained by the community service team. The result of this activity is that teachers find it very easy and quick to understand how to use these applications for their teaching and learning activities. They hope that online learning activities using the website-based Content Management System method will continue to be carried out as future works.
A Convolutional Neural Network Model for Mushroom Toxicity Recognition Irvanizam, Irvanizam; Subianto, Muhammad; Jamil, Muhammad Salsabila
Infolitika Journal of Data Science Vol. 3 No. 2 (2025): November 2025
Publisher : Heca Sentra Analitika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60084/ijds.v3i2.359

Abstract

Mushroom poisoning remains a public health concern, often caused by misidentifying toxic species that visually resemble edible ones. This study investigates the feasibility of using a Convolutional Neural Network (CNN) to classify five mushroom species, Amanita caesarea, Amanita phalloides, Cantharellus cibarius, Omphalotus olearius, and Volvariella volvacea into toxic and non-toxic categories based on image data. A dataset of 137 images was collected and preprocessed through resizing, normalization, and data augmentation. A modified AlexNet-based CNN was trained and evaluated using accuracy, precision, recall, and F1-score. The best-performing model achieved a validation accuracy of 0.40, indicating limited discriminative capability. These findings highlight that the dataset size is insufficient for training a CNN from scratch and that the model cannot reliably distinguish species with subtle morphological differences. The study concludes that larger datasets, improved image quality, and transfer learning approaches are essential for achieving practical and deployable mushroom classification performance.
Adaptive Heuristic-Based Ant Colony Optimization for Multi-Constraint University Course Timetabling with Morning Slot Preference for Energy Efficiency Muslem, Imam; Irvanizam, Irvanizam; Almuzammil, Almuzammil; Johar, Farhana
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5588

Abstract

University course timetabling is a well-known NP-hard combinatorial optimization problem that involves multiple interacting constraints, including lecturer availability, classroom capacity, time-slot allocation, and course duration. Most existing metaheuristic-based approaches primarily focus on eliminating academic conflicts, while contextual and operational aspects, such as energy efficiency, are rarely considered explicitly. In addition, standard Ant Colony Optimization (ACO) methods often suffer from premature convergence and limited adaptability during the solution search process. This study proposes an Adaptive Heuristic-Based Ant Colony Optimization (AHB-ACO) approach for multi-constraint university course timetabling with a particular emphasis on morning slot preference as an energy efficiency proxy. The proposed method extends the conventional ACO framework by integrating an adaptive heuristic mechanism that dynamically guides the solution construction process toward compact and conflict-free schedules, while simultaneously favoring morning time slots to support reduced classroom cooling demand. Hard constraints, including lecturer and room conflicts, are strictly enforced, whereas the temporal preference is modeled as a soft constraint. The performance of AHB-ACO is evaluated through extensive scheduling simulations using academic datasets under various parameter settings. Experimental results demonstrate that the proposed approach consistently produces conflict-free timetables, achieving a conflict function value of C(S)=0 with stable convergence behavior. Furthermore, parameter sensitivity analysis indicates that AHB-ACO exhibits good robustness with respect to variations in the number of ants and iterations, showing a reasonable trade-off between solution quality and computational time. Additional analysis reveals an increased utilization of morning time slots compared to non-optimized schedules, indicating the effectiveness of the proposed energy-aware preference. Overall, the results suggest that AHB-ACO provides an effective and adaptive solution for university course timetabling that not only satisfies academic constraints but also addresses operational considerations related to energy efficiency.
PERFORMANCE ANALYSIS OF MACHINE LEARNING AND INDOBERT IN CLASSIFYING SENTIMENTS ON INDONESIA'S FREE NUTRITIOUS MEAL Maulyanda; Nazhifah, Sri Azizah; Pane, Syafrial Fachri; Irvanizam, Irvanizam
CYBERSPACE: Jurnal Pendidikan Teknologi Informasi Vol 10 No 1 (2026)
Publisher : Universitas Islam Negeri Ar-Raniry Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/cj.v10i1.33886

Abstract

Natural Language Processing (NLP) is a branch of artificial intelligence that is widely used to analyze whether a sentence contains positive, negative, or neutral sentiment, particularly in the context of expressing opinions in the online environment. This study compares several models to identify the most optimal one, namely Naïve Bayes, Support Vector Machine (SVM), XGBoost, and IndoBERT. The dataset used in this research was obtained from Kaggle and consists of 5,644 data points in the neutral class, 2,934 data points in the positive class, and 2,606 data points in the negative class. Prior to model implementation, the dataset underwent a preprocessing stage that included case folding, cleansing, tokenization, stemming, and stopword removal. Subsequently, the data were trained using the four aforementioned methods. The results indicate that Naïve Bayes achieved an accuracy of 75%, SVM reached 79%, XGBoost obtained 76%, while IndoBERT achieved the highest accuracy at 85%. Therefore, it can be concluded that, using this dataset, IndoBERT performed sentiment classification very effectively.
Co-Authors . Zulfan Abas, Abdul Hawil Ahmad, Noor Atinah Almuzammil, Almuzammil Aprianto . Azhar, Fauzul Bahri, Ridzky Aulia Dharma, Dian Budi Erkata Yandri Fachry Abda El Rahman Farsiah, Laina Ghazi Mauer Idroes Hadinata, Edrian Hizir Sofyan Idroes, Ghalieb Mutig Irfan Sudahri Damanik Jamil, Muhammad Salsabila Johar, Farhana Kurniadinur, Kurniadinur Kurniawaty, Risma Kusumo, Fitranto Lala, Andi Marwan Marwan Maulana, Aga Maulyanda, Maulyanda Misbullah, Alim Muhammad Subianto Muhammad Yanis Muhammad Yusuf Mursyida, Waliam Muslem Muslem Muslem R, Imam Nazhifah, Sri Azizah Nizamuddin Nizamuddin Nurleila, Nurleila Rahman, Isra Farliadi Raudhatul Jannah Razief Perucha Fauzie Afidh Rinaldi Idroes Safhadi, Aulia Al-Jihad Saputra, Kurnia Sasmita, Novi Reandy sufriani, sufriani Sugara, Dimas Rendy Suhendra, Rivansyah Syafrial Fachri Pane, Syafrial Fachri Tatsara, Nidya Taufiq Karma Teuku Rizky Noviandy TRINA EKAWATI TALLEI Usman, Tarmizi Vikki, Zakial Yolanda Yolanda, Yolanda Zahriah, Zahriah Zhilalmuhana, Teuku Zulkarnain Jalil