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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JOURNAL OF APPLIED INFORMATICS AND COMPUTING JOURNAL OF INFORMATICS AND COMPUTER SCIENCE International Journal of Engineering, Science and Information Technology Jurnal Tika Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) TECHSI - Jurnal Teknik Informatika Jurnal Teknologi Terapan and Sains 4.0 INOVTEK Polbeng - Seri Informatika Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Risawandi, Risawandi
Universitas Malikussaleh
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy 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 Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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SISTEM PENDETEKSIAN DAN PENGENALAN EKSPRESI PADA WAJAH SECARA REAL-TIME MENGGUNAKAN FITUR HARALICK DAN FITUR HAAR Risawandi, Risawandi; Olivia, Karina
Jurnal Teknologi Terapan and Sains 4.0 Vol 3 No 1 (2022): Jurnal Teknologi Terapan & Sains
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/tts.v3i1.8584

Abstract

Mendeteksi dan mengenali ekspresi wajah adalah tugas yang sangat sulit. Pelacakan objek wajah secara realtime disebabkan oleh sifat dan lokasi yang terbatas di mana ia terjadi. Pengenalan wajah adalah langkah utama dalam sistem pengenalan wajah. Deteksi wajah berarti bahwa gambar tertentu diproses untuk menentukan wajah manusia, posisi dan ukurannya, serta keakuratan posisi itu secara langsung mempengaruhi efek deteksi wajah. Saat ini, metode pengenalan wajah terutama didasarkan pada metode fitur geometris, pendekatan berbasis model warna kulit, dan metode berbasis teori statistik. Karena perkembangan teknologi citra digital begitu pesat, maka dari itu perlu dikembangkannya sebuah kecerdasan buatan untuk pendeteksian pengenalan ekspresi pada wajah secara realtime. Dalam kasus tersebut peneliti tertarik untuk mencoba ekstrasi fitur haralick dan fitur haar dalam pendeteksian dan pengenalan ekspresi wajah secara realtime dengan menggunakan pemodelan haarcascade untuk klasifikasinya. Dalam penelitian ini hasil implementasi yang sudah dilakukan dari data testing menggunakan fitur haralick dengan ekspresi senang nilai persentasenya 94.429%, ekspresi sedih persentasenya 38.777%, ekspresi marah persentasenya 49.3777%. lalu data testing menggunakan fitur haar dengan ekspresi senang nilai persentasenya 78.329%, ekspresi sedih persentasenya 36.292%, ekspresi marah persentasenya 39.517%.
Implementation of an Artificial Neural Network Algorithm for Mental Illness Virtual Assistant Chatbot Development iqbal, Muhammad; darnila, eva; risawandi
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/wkj2ks31

Abstract

Mental health is a critical issue in modern society, yet access to psychological support remains limited. This study presents the development of a chatbot as a virtual assistant for individuals experiencing mental illness using the Artificial Neural Network (ANN) algorithm. The dataset was manually constructed and divided using an 80:20 ratio for training and testing. The ANN model employs one hidden layer with ReLU and softmax activation functions to classify user input into relevant mental health categories. The model achieved a training accuracy of 83.2% with a loss of 0.655, and a testing accuracy of 81.5%, indicating solid performance. Compared to rule-based methods, ANN provides better adaptability in recognizing diverse expressions and delivering context-aware, empathetic responses. This study also introduces a custom-built mental health dataset and integrates a crisis response module that is underexplored in previous research. The chatbot targets five categories of mental disorders: Schizophrenia, Bipolar Disorder, Depression, Anxiety, and Personality Disorders. Findings suggest that ANN-based chatbots can serve as reliable, accessible, and scalable early-stage mental health support tools.
Smart Valve Irrigation System Using Fuzzy Logic for Mustard Pranidana, Abdi Mulia; Qamal, Mukti; Risawandi, Risawandi
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10024

Abstract

This study presents the design and implementation of a smart irrigation system using Mamdani fuzzy logic integrated with IoT-based environmental sensors. The system utilizes an ESP32 microcontroller, DHT22 temperature sensor, capacitive soil moisture sensor, and a solenoid valve to perform adaptive irrigation based on real-time environmental conditions. The fuzzy logic engine processes sensor inputs and determines the irrigation intensity through centroid-based defuzzification. A web-based dashboard was developed using PHP and JavaScript to monitor temperature, soil moisture, and irrigation status in real time. The system was tested on mustard greens (Brassica juncea L.) for 12 hours, resulting in a 35% water usage reduction compared to manual watering methods while maintaining optimal soil moisture. This approach demonstrates a promising solution for sustainable and efficient smart agriculture.
Clustering Coastal Areas Based on Aquaculture Productivity in North Aceh Regency Using K-Means Algorithm Ulfa, Septia Mulya; Dinata, Rozzi Kesuma; Risawandi, Risawandi
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10094

Abstract

This study aims to cluster coastal subdistricts in North Aceh Regency based on the productivity of seven key aquaculture commodities milkfish, vannamei shrimp, tiger shrimp, tilapia, mojarra, grouper, and crab using the K-Means algorithm. The dataset, sourced from 15 coastal subdistricts, was normalized using the Z-Score method. The optimal number of clusters was determined using the Elbow Method, and clustering performance was evaluated with the Silhouette Score, yielding a value of 0.5293, indicating a moderately well-defined structure. The resulting clusters reflect distinct productivity levels: Cluster 0 (low), Cluster 1 (moderate), and Cluster 2 (high). A two-dimensional PCA plot was used to visualize the clusters, showing clear separations among them. These findings offer valuable insights for regional planners and policymakers in developing targeted aquaculture strategies and optimizing resource allocation, particularly for underperforming areas.
DECISION SUPPORT SYSTEM USING WEIGHTED PRODUCT METHOD IN CHIPS MATERIAL SELECTION (CASE STUDY: HASTI FAMILY CHIPS BUSINESS) Luqman Nul Hakim; Safwandi; Risawandi
Multidiciplinary Output Research For Actual and International Issue (MORFAI) Vol. 4 No. 4 (2024): Multidiciplinary Output Research For Actual and International Issue
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/morfai.v4i4.2406

Abstract

This study designs a decision support system to help the owner of the Hasti Family chips business choose the optimal raw materials in making cassava, banana, and breadfruit chips. The Weighted Product (WP) method is used as a multi-criteria decision-making method by considering criteria such as chip color, chip texture, chip taste, chip durability and fruit price. Criteria data and alternative raw materials are processed using WP calculations to produce the best alternative ranking. The result is a web-based decision support system that implements the WP method, presents an interface for entering data and displays the best alternative ranking. This system improves the efficiency of decision-making, minimizes the risk of selecting inappropriate raw materials, improves product quality, and supports business growth. The results of the research on the decision support system for selecting chips ingredients show that this system determines the best ingredients by finding the final value of the V vector search from 3 cassava data, 3 banana data and 3 breadfruit data that will be entered into the system and get results from butter cassava, which has the highest V value of 0.38311467, followed by wak banana with an impressive V value of 0.398763354, and Bali breadfruit, which has a prominent V value of 0.350015233. The conclusion of this study is that the designed application is able to optimize the process of selecting raw materials for chip production more efficiently, quickly, and this system not only accelerates decision making but also ensures more structured and reliable data recording.
Utilization of Radio Frequency for Monitoring the Temperature of Hydroponic Plants Based on the Internet of Things Dara Maulidia; Eva Darnila; RisaWandi
Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Vol. 2 (2024): Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Publisher : Faculty of Engineering, Malikussaleh University

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

Abstract

This research aims to develop a temperature monitoring system for hydroponic plants based on the Internet of Things (IoT), using the nRF24L01 radio frequency module. The system is designed to monitor the temperature of hydroponic plants, specifically spinach and chili, and to evaluate its range and efficiency compared to traditional methods such as GSM and Wi-Fi. With this technology, users can monitor and control the hydroponic environment in real-time through a smartphone, which can enhance the efficiency and effectiveness of plant cultivation. The research results indicate that the radio frequency module has a better data transmission range and higher reliability compared to traditional methods, making it an effective and practical solution for hydroponic monitoring. Additionally, this system provides significant benefits in improving plant growth and productivity by ensuring optimal environmental conditions. The use of IoT and radio frequency technology in this research demonstrates great potential for broader applications in agriculture, particularly in promoting more sustainable and efficient farming practices. These findings open up opportunities for further development in utilizing technology to support modern, connected, and smart agriculture.
OPTIMASI JUMLAH CLUSTER PADA K-MEANS CLUSTERING MENGGUNAKAN PARTICLE SWARM OPTIMIZATION UNTUK PENGELOMPOKAN UKT MAHASISWA Ira Fazira; Zahratul Fitri; Risawandi
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6396

Abstract

The determination of the Single Tuition Fee (UKT) group in higher education faces challenges in terms of distribution fairness due to the inappropriate grouping of students' socio-economic conditions. The K-Means algorithm, while effective in handling large-scale data at good computational speeds, has a drawback in determining the optimal number of clusters automatically. This study aims to implement the integration of Particle Swarm Optimization (PSO) with K-Means Clustering in the grouping of student UKT data and evaluate the improvement of the quality  of clustering produced compared to conventional methods. The study uses a dataset of 437 new students of the Faculty of Engineering in 2024 from Malikussaleh University with 8 attributes that describe family socioeconomic conditions. The research stages include pre-processing of data, determination of the optimal number of clusters using PSO, implementation of K-Means clustering with optimal K, model evaluation using Silhouette Coefficient and Davies-Bouldin Index, and model comparison using the elbow method. The results of the study showed that PSO succeeded in determining the optimal number of clusters as many as 3 clusters. The implementation of K-Means with K=3 resulted in the distribution of clusters: cluster 0 (40 students/9.2%), cluster 1 (93 students/21.3%), and cluster 2 (304 students/69.6%). Clustering quality evaluation  resulted in  a Silhouette Coefficient of 0.278062 and  a Davies-Bouldin Index of 1.430505 indicating adequate cluster formation with fairly good internal cohesion and reasonable separation between clusters. Comparison with  the conventional K-Means method  using the Elbow Method shows the advantage of PSO-K-Means with  a higher Silhouette Coefficient (0.278062 vs 0.250300) and  a competitive Davies-Bouldin Index (1.430505 vs 1.315400). This research proves that the combination of PSO and K-Means can provide a more optimal solution in the grouping of student UKT to support a fairer determination of tuition fees based on family economic ability.
PENERAPAN METODE ALGORITMA SVM (SUPPORT VECTOR MACHINE) UNTUK KLASIFIKASI PENDERITA PENYAKIT GASTROESOPHAGEAL REFLUX DISEASE: APPLICATION OF SVM (SUPPORT VECTOR MACHINE) ALGORITHM METHOD FOR CLASSIFICATION OF GASTROESOPHAGEAL REFLUX DISEASE PATIENTS Teuku Ferynanda Ramadhan; Asrianda; Risawandi
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6466

Abstract

Gastroesophageal Reflux Disease (GERD) is a digestive disorder caused by the backflow of stomach acid into the esophagus, with symptoms that often resemble those of other conditions, making diagnosis challenging. This study aims to implement the Support Vector Machine (SVM) algorithm to develop a classification system for GERD patients based on clinical symptom data, including chest pain, swallowing disorders, regurgitation, and others. The research was conducted at Sakinah General Hospital in Lhokseumawe City using patient data from the 2020–2023 period. The classification system was designed through a series of stages including data preprocessing, normalization, and the application of a polynomial kernel in the SVM algorithm. The results demonstrate that the SVM algorithm achieved an accuracy of 82.5% and an F1-score of 58.3%, indicating a strong classification performance in distinguishing between GERD and non-GERD patients, and suggesting its potential as an effective diagnostic support tool for medical professionals.
PERAN PENTING IT TERHADAP PERKEMBANGAN DUNIA DI ERA REVOLUSI INDUSTRI 4.0 Risawandi Risawandi
Jurnal Teknologi Terapan and Sains 4.0 Vol 6 No 3 (2025): Jurnal Teknologi Terapan & Sains
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/tts.v6i3.26349

Abstract

Di dunia developer atau dunianya pengembangan system bertemu beberapa macam jabatan, ada yang menjadi project manager, desainer, konsultan, dan system analyst. Penggunaan teknologi oleh manusia diawali dengan pengubahan sumber daya alam menjadi alat-alat sederhana. Jaman sekarang yang teknologi adalah salah satu sangat dibutuhkan oleh manusia, karena sekarang banyak hal-hal yang dikaitkan dengan teknologi, dan juga sangat membantu manusia dalam segi apapun. Penelitian ini menggunakan pendekatan kuantitatif dengan metode penelitian deskriptif. Pengumpulan data dilakukan dengan metode survei dengan menggunakan instumen kuesioner yang bertujuan untuk mendapatkan gambaran secara sistematis karakteristik populasitertentu atau bidangtertentu secara faktual dan cermat. Teknologi telah memengaruhi masyarakat dan sekelilingnya dalam banyak cara. Di banyak kelompok masyarakat, teknologi telah membantu memperbaiki ekonomi (termasuk ekonomi global masa kini) dan telah memungkinkan bertambahnya kaum senggang. Banyak proses teknologi menghasilkan produk sampingan yang tidak dikehendaki yang disebut pencemar dan menguras sumber daya alam, merugikan, dan merusak Bumi dan lingkungannya. Kata Kunci : Teknologi, Global, Manusia
Implementation of an Artificial Neural Network Algorithm for Mental Illness Virtual Assistant Chatbot Development Muhammad iqbal; eva darnila; risawandi
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/wkj2ks31

Abstract

Mental health is a critical issue in modern society, yet access to psychological support remains limited. This study presents the development of a chatbot as a virtual assistant for individuals experiencing mental illness using the Artificial Neural Network (ANN) algorithm. The dataset was manually constructed and divided using an 80:20 ratio for training and testing. The ANN model employs one hidden layer with ReLU and softmax activation functions to classify user input into relevant mental health categories. The model achieved a training accuracy of 83.2% with a loss of 0.655, and a testing accuracy of 81.5%, indicating solid performance. Compared to rule-based methods, ANN provides better adaptability in recognizing diverse expressions and delivering context-aware, empathetic responses. This study also introduces a custom-built mental health dataset and integrates a crisis response module that is underexplored in previous research. The chatbot targets five categories of mental disorders: Schizophrenia, Bipolar Disorder, Depression, Anxiety, and Personality Disorders. Findings suggest that ANN-based chatbots can serve as reliable, accessible, and scalable early-stage mental health support tools.
Co-Authors Angaw, Mariance Wulan Asrianda Asrianda Asrianda Asrianda Asrul Fahmi Bustami Bustami Bustami Bustami Dahlan Abdullah Dara Maulidia Eva Darnila Eva Darnila Fadlisyah Fadlisyah Fuadi, Wahyu Furqan, Hafizul Gunarwan Gunarwan Gunarwan, Gunarwan Halimatun Sakdiah Halimatun Sakdiah, Halimatun Helmi Naluri Ira Fazira Khainesya Khainesya Lidya Rosnita Luqman Nul Hakim M. Irsan Maulana Amri Muhammad Fikry Muhammad Iqbal Muhammad Iqbal Muhammad Wahyu Pohan Mukti Qamal mulkal razali Mutammimul Ula Nurdin Nurdin Nurhaliza Bin Aras Nursanti Nursanti Nursanti Nursanti Nurul Aflah Nurul Aflah Olivia, Karina Pranidana, Abdi Mulia Rian Kelana Putra Rina Julita Riska Yanti RISKA YANTI, RISKA Rizal S.Si., M.IT, Rizal Rosdian Rosdian dian rosdian Rozzi Kesuma Dinata Rozzy Rozzy Safwandi Safwandi Senda, Tito Afwi Teuku Ferynanda Ramadhan Tito Afwi Senda Ulfa, Septia Mulya Violita Aditya Zahrah Wahyu Fuadi Wahyu Fuadi Yesy Afrillia Zahratul Fitri Zainannur Nasution Zakiatul Husna