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Risawandi, Risawandi
Universitas Malikussaleh
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Implementation of Organic and Inorganic Waste Selection System Based on Internet of Things Using MQTT Protocol at Abby Lhokseumawe Hospital Julita, Rina; Darnila, Eva; Risawandi, Risawandi
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.826

Abstract

The waste sorting system designed for Abby Hospital in Lhokseumawe aims to improve the efficiency and effectiveness of waste management by automatically separating organic and inorganic materials. This system integrates Proximity sensors as the primary detectors, capable of detecting organic objects within a spatial range of 4 cm and inorganic objects within a range of 5 cm. The main feature of this system is its ability to automatically sort waste, which helps reduce the potential for human error in waste categorization and improve operational efficiency in the waste disposal process. During the testing phase, which focuses on assessing the trash bin's capacity when complete, the system uses ultrasonic sensors to measure and monitor the waste filling levels. The test results show an average data transmission delay of 445.33 ms, which is within the acceptable tolerance for this system. Additionally, the prototype is equipped with an operational status notification feature for users. This notification is delivered with an average delay of just 402.5 ms, ensuring that system status information is provided to users in real time. The combination of sensor detection precision and response speed in the waste sorting process highlights the system's effectiveness in improving waste management at the hospital. This system is expected to support the hospital's efforts in maintaining a clean environment and contribute to a more environmentally friendly and organized waste management program.
Data Mining Analysis for Clustering the Number of Tb Patients in North Aceh Health Centers Using the Spectral Method Clustering Khainesya, Khainesya; Darnila, Eva; Risawandi, Risawandi
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.847

Abstract

Tuberculosis (TB) is one of the infectious diseases that is a significant concern in the world of health, especially in the North Aceh region. Grouping the number of TB patients based on severity and region is very important to support decision-making in further prevention and treatment efforts. This study applies the Spectral Clustering method to cluster the number of TB patients at Baktiya Health Center, Bayu Health Center, and Lhoksukon Health Center to identify patient distribution patterns based on severity categories. The system built is a web-based data mining analysis system using PHP and MySQL as a database. Clustering is done by dividing patients into three categories, low, medium, and high, based on five main criteria, namely age, gender, month of treatment, diagnosis results, and patient address. The results showed that Lhoksukon Health Center had the highest number of TB patients, with 136 patients (37.06%), an average age of 48.6 years, and the most cases occurred in December 2022. Bayu Health Center was at a moderate level with 130 patients (35.42%), most of whom were 45.5 years old, and most cases occurred in November 2023. Meanwhile, Baktiya Health Center had the lowest number of patients, 101 (27.52%), with the most cases occurring in November. From the clustering results, it can be concluded that the Spectral Clustering method can group TB patients well to help medical personnel and related parties develop more effective intervention strategies based on the region and severity of the patient.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN LAPTOP PADA E-COMMERCE MENGGUNAKAN METODE SIMPLE MULTI ATTRIBUTE RATING TECHNIQUE Furqan, Hafizul; Risawandi, Risawandi; Rosnita, Lidya
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.v3i1.6851

Abstract

Virus corona yang mewabah di Indonesia telah mengubah berbagai aspek dalam kehidupan, salah satunya adanya perubahan yang signifikan pada ekonomi digital. Masyarakat yang awalnya bertransaksi di toko konvensional mulai beralih ke toko digital, namun beberapa kelompok masyarakat masih kesulitan pada saat membeli laptop di e-commerce, karena tidak dapat memeriksa langsung produk dengan spesifikasi yang dibutuhkan. Oleh karena itu, penulis merancang suatu sistem pendukung keputusan yang dapat membantu untuk merekomendasikan laptop yang sesuai secara efektif dan efisien. Beberapa kriteria yang digunakan adalah rating produk, kondisi barang, harga, merek, garansi, tipe prosesor, kapasitas memori (RAM), tipe penyimpanan, kapasitas penyimpanan, kartu grafis, ukuran layar, dan sistem operasi. Simple Multi Attribute Rating Technique (SMART) adalah metode yang digunakan dalam proses perhitungan, pembobotan yang dapat dilakukan secara fleksibel merupakan kelebihan dari metode SMART yang mempermudah masyarakat memberikan nilai terhadap masing-masing kriteria berdasarkan preferensi yang diinginkan, nilai bobot juga tidak akan berpengaruh jika ada penambahan ataupun pengurangan alternatif. Setelah menggunakan 20 data alternatif dari salah satu e-commerce (shopee), sistem berhasil memberikan keluaran berupa peringkat setiap alternatif dan hasil yang didapat menunjukkan nilai alternatif tertinggi sebesar 0,639841521 dan nilai terkecil sebesar 0,345627506.
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): Juli
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.
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.
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.
Co-Authors Angaw, Mariance Wulan Asrianda Asrianda Asrianda Asrianda Asrul Fahmi Bustami Bustami Bustami Bustami Dahlan Abdullah Eva Darnila Fadlisyah Fadlisyah Fuadi, Wahyu Furqan, Hafizul Gunarwan Gunarwan Gunarwan, Gunarwan Halimatun Sakdiah Halimatun Sakdiah, Halimatun Helmi Naluri Julita, Rina Khainesya, Khainesya Lidya Rosnita Luqman Nul Hakim M. Irsan Maulana Amri Muhammad Fikry 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 Riska Yanti RISKA YANTI, RISKA Rizal S.Si., M.IT, Rizal Rosdian dian rosdian rosdian, Rosdian dian Rozzi Kesuma Dinata Rozzy Rozzy Safwandi Senda, Tito Afwi Tito Afwi Senda Ulfa, Septia Mulya Wahyu Fuadi Wahyu Fuadi Yesy Afrillia Zahrah, Violita Aditya Zainannur Nasution Zakiatul Husna