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All Journal Jurnal Pengabdian UntukMu NegeRI RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JURNAL MEDIA INFORMATIKA BUDIDARMA IT JOURNAL RESEARCH AND DEVELOPMENT Dinamisia: Jurnal Pengabdian Kepada Masyarakat SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Teknologi Terapan Indonesian Journal of Electrical Engineering and Computer Science Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) Jurnal PkM (Pengabdian kepada Masyarakat) Jurnal Gramatika: Jurnal Penelitian Pendidikan Bahasa dan Sastra Indonesia Journal of Software Engineering and Information System (SEIS) JITER-PM (Jurnal Inovasi Terapan - Pengabdian Masyarakat)
Harun Mukhtar
Universitas Muhammadiyah Riau
Religion Agriculture, Biological Sciences & Forestry Arts Humanities 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 Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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ANALISIS KESUBURAN PERTANIAN MELALUI IRIGASI DENGAN MENGGUNAKAN METODE K-MEANS CLUSTERING Mukhtar, Harun; Syafutri, Trimaiyuza Maulina; Rahman, Rayhan Aulia; Putra, Afyuadri; Hafsari, Rizka
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 4 No. 2 (2024)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v4i2.7599

Abstract

Indonesia is an agricultural country where the majority of its population makes a living from agriculture. The agricultural sector is a very important sector for economic development in an agricultural country like Indonesia. Poor irrigation facilities greatly affect the results of the agricultural sector. Crop quality is based on many factors such as the characteristics of the irrigation process, including the amount of air and irrigation time. Overwatering irrigation can cause air wastage, soil freezing disease, yellowing of plant leaves, wilting of plant leaves, and many other problems. K-Means clustering is a method used to group data into one or more groups or clusters. The advantages of the K-Means algorithm are that it is easy and simple to implement, scalability, speed in convergence, and the ability to adapt to sparse data. K-Means to group agricultural land based on soil fertility and rainfall data, found that this grouping can help in more efficient irrigation planning. The clustering results show that agricultural land can be divided into three main clusters based on soil fertility and irrigation. Soil fertility is formed into three clusters based on the level of soil fertility using the Kmeans algorithm which can also be effective in helping in the Indonesian agricultural sector. By adding technological elements, the results provided will of course be even better.
ALGORITMA K-MEANS UNTUK PENGELOMPOKAN PERILAKU CUSTOMER Mukhtar, Harun; Dwi Pramaditya, Ilham; Saputra Weisdiyanto, Wahyu; Hardian_Putra, Saddam; Trimuawasih, Diana; Auralia Rilda, Azzahra
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 4 No. 2 (2024)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v4i2.7615

Abstract

In the rapidly evolving digital era, understanding customer purchasing behavior is crucial for marketing strategies and business development. This study uses the K-means clustering algorithm to analyze and segment customer purchasing behavior. This algorithm effectively partitions data into groups based on similar characteristics. The aim of this study is to identify purchasing behavior patterns using attributes such as purchase frequency, expenditure amount, and product types. By segmenting customers into homogeneous groups, companies can design more effective marketing strategies and better personalization. The results show that the K-means clustering method successfully segments customers based on similar behavior patterns, which can be used for market segmentation and strategy development. The application of this algorithm in purchasing behavior analysis is expected to provide deep insights and support better business decision-making, offering a competitive advantage for companies.
TEKNIK MACHINE LEARNING UNTUK ANALISA KLASIFIKASI KUALITAS UDARA: A REVIEW Alfian, Haris; Wahyuni, Sri; Revalino, Aqil; Mirano, M. Fitter; Rahmayana, Elsa; Mukhtar, Harun
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 4 No. 2 (2024)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v4i2.7617

Abstract

Air quality has a significant impact on human health and the environment, making its monitoring and classification extremely important. This review explores the application of machine learning techniques in analyzing and classifying air quality. Various methods such as decision trees, support vector machines, neural networks, and ensemble learning are evaluated to assess their effectiveness in processing complex and multidimensional air sensor data. This study also discusses challenges in data collection and preprocessing, selection of relevant features, and interpretation of classification results. Furthermore, this review identifies recent trends and future research opportunities in the use of machine learning to improve the accuracy and efficiency of air quality monitoring systems. The analysis results show that machine learning techniques have great potential to enhance our understanding of air quality dynamics and support better decision-making in environmental management
Bank Sampah sebagai Alternatif Strategi Pengelolaan Sampah Berbasis Masyarakat di Desa Kerubung Jaya Mukhtar, Harun; Rahma Dayani, Lilian; Tiara Putri, Rahma; Triana Dahar, Ulya; Arviero L Tobing, Yandi; Dermawan, Aldi; Prayoga, Yuda; Marcelino, Ananda; Fatma, Yulia
JITER-PM (Jurnal Inovasi Terapan - Pengabdian Masyarakat) Vol. 2 No. 2 (2024): JITER-PM
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/jiter-pm.v2i2.6221

Abstract

Salah satu permasalahan besar yang dialami kota-kota besar di Indonesia adalah persampahan. Sampah dapat diartikan sebagai konsekuensi adanya aktivitas kehidupan manusia. Kegiatan pengurangan sampah bertujuan agar seluruh lapisan masyarakat, baik pemerintah, dunia usaha, maupun masyarakat luas ; Reduce, Reuse, dan Recycle (3R) melalui upaya-upaya cerdas efisien dan terprogram. Kegiatan pengabdian ini berfokus pada penanggulangan sampah botol plastik yang ingin di daur ulang. Namun, kami juga membuat bank sampah untuk sampah organik dan non organik agar nantinya lebih mudah dibakar ataupun di daur ulang.
Deep Learning untuk mendeteksi gangguan lambung melalui citra iris mata Mukhtar, Harun; Baidarus; Aryanto, Eggy; Saputra Sy, Yandiko
Computer Science and Information Technology Vol 4 No 3 (2023): Jurnal Computer Science and Information Technology (CoSciTech)
Publisher : Universitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/coscitech.v4i3.6392

Abstract

The stomach is one of the essential organs of the human digestive system. If the stomach organ cannot work typically, it will cause problems. This is a disease that occurs in the stomach organs. Gastric disease also occurs due to a lack of knowledge about stomach disease, so people ignore the symptoms that arise. Gastric disease is a disease that is considered very serious. If left alone, it can cause other diseases to occur. Generally, finding out the presence of stomach disease is still done manually, and several tests are carried out when stomach disease has recurred. Gastric disorders were classified using 360 iris images taken manually via a digital camera and a web database of iris images. The author used the Radial Basis Function Neural Network (RBFNN) method to classify iris images of patients with gastric disorders in this study. The results obtained from this research can organize the iris images of people with gastric disturbances. Classification of iris images of patients with gastric disorders achieved a training accuracy rate of 65.00%.
Feature selection technique on convolutional neural network – multilabel classification task Hayami, Regiolina; Yusoff, Nooraini; Daud, Kauthar Mohd; Mukhtar, Harun; Al Amien, Januar
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp2001-2009

Abstract

Automated text-based recommendation, an artificial intelligence development, finds application in document analysis like job resumes. The classification of job resumes poses challenges due to the ambiguity in categorizing multiple potential jobs in a single application file, termed multi-label classification, deep learning, particularly convolutional neural networks (CNN), offers flexibility in enhancing feature representations. Despite its robust learning capabilities, the black-box design of deep learning lacks interpretability and demands a substantial number of parameters, requiring significant computational resources. The primary challenge in multilabel learning is the ambiguity of labels not fully explained by traditional equivalence relations. To address this, the research employs feature selection techniques, specifically the Chi-square method. The goal is to reduce features in deep learning models while considering label relevance in multi-label text classification, easing computational workload while preserving model performance. Experimental tests, both with and without the Chi-square feature selection technique on the dataset, underscore its substantial impact on the classification model's ability. The conclusion emphasizes the influence of the Chi-square feature selection technique on performance and computational time. In summary, the research underscores the importance of balancing computational efficiency and model interpretability, especially in complex multi-label classification tasks like job applications.
IMPLEMENTASI ALGORITMA A STAR DALAM PENCARIAN RUTE TERPENDEK (SHORTEST PATH PROBLEM) PADA SISTEM PENCARIAN KANTOR POS DI KOTA PEKANBARU Mukhtar, Harun; Hendri, Yusriadi; Soni
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 2 No. 1 (2022)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (716.156 KB) | DOI: 10.37859/seis.v2i1.3313

Abstract

With the advancement of information technology today, there are several solutions that can facilitate the search for the shortest path (Shortest Path Problem) by using various algorithms such as the djiktra algorithm, A star algorithm, floyd warshall algorithm, prim algorithm and others. Algorithm A* (A star) is one of the algorithms included in the category of search methods that have information (informed search method). This algorithm is very good as a solution to the path finding process where this algorithm looks for the distance of the fastest route that will be taken by an initial point (starting point) to the destination object. The search technique used in this simulation is using the A* Algorithm with the manhattan distance heuristic function. Path Finding is one of the most important materials in Artificial Intelligence. Path Finding is usually used to solve problems on a graph. This study aims to provide a solution in finding the shortest route, so as to reduce operational costs that must be incurred by the company and also with this new system, it can be known the distance from one point to another without using manual calculations.
Peningkatan Pengetahuan dan Sikap Lansia terhadap Kesehatan Mental Gasril, Pratiwi; Silvia Elki Putri; Harun Mukhtar; Alfaizun Nur Alfidin; Dhea Dahliana Amanda; Ilham Dwi Pramaditya
Jurnal Pengabdian UntukMu NegeRI Vol. 8 No. 3 (2024): Pengabdian Untuk Mu negeRI
Publisher : LPPM UMRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jpumri.v8i3.8150

Abstract

Kesehatan mental merupakan aspek penting dalam menjaga kualitas hidup lansia. Namun, rendahnya pengetahuan dan sikap positif terhadap kesehatan mental sering menjadi hambatan dalam mencapainya. Diperkirakan 121 juta manusia di muka bumi mengalami masalah Kesehatan Mental. Sejauh ini, prevalensi masalah Kesehatan Mental salah satunya depresi pada lansia di dunia berkisar 8-15% dan hasil meta analisis dari laporan negara-negara di dunia mendapatkan prevelensi rata-rata depresi pada lansia adalah 13,5% dengan perbandingan antara perempuan dan laki-laki yaitu 14,1:18,6. Kegiatan pengabdian ini bertujuan untuk mengevaluasi efektivitas program edukasi di Sekolah Lansia dalam meningkatkan pengetahuan dan sikap lansia terhadap kesehatan mental. Metode pengabdian menggunakan pendekatan kuantitatif dengan desain pretest-posttest. Sebanyak 30 lansia dilibatkan sebagai responden. Intervensi dilakukan melalui serangkaian sesi edukasi yang mencakup pengenalan kesehatan mental, pengelolaan stres, serta teknik peningkatan kesejahteraan kesehatan mental lansia. Hasil dari kegiatan ini menunjukkan adanya peningkatan pengetahuan dan sikap lansia terhadap kesehatan mental. Nilai pre pada pengetahuan lansia yaitu 60,0% dan meningkat yang dilihat dari hasil post test yaitu 96,7%. Begitu juga dengan sikap juga mengalami peningkatan yaitu dari 30,0% (nilai pre test) menjadi 96,7% (nilai post test)
OPTIMISASI ALGORITMA K-MEANS DENGAN METODE REDUKSI DIMENSI UNTUK PENGELOMPOKAN BIG DATA DALAM ARSITEKTUR CLOUD COMPUTING Putra, Bayu Anugerah; Mukhtar, Harun; Br Bangun, Elsi Titasari; Gusnanda, Alris; Maisyarah, Adila; Kurniawan, Muhammad Irgi; Pradipa, Raditya; Ali, Zurrahman Muhammad
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 1 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i1.7616

Abstract

In the era of big data, data clustering becomes a major challenge due to the complexity and huge volume of data. The K-means algorithm is one of the clustering techniques that is often used due to its simplicity. However, K-means faces difficulties in handling high-dimensional and large-volume data. This study proposes an optimization of the K-means algorithm using the Principal Component Analysis (PCA) dimensionality reduction method to improve the efficiency and accuracy of big data clustering in cloud computing architecture. The KDD Cup 1999 dataset is used to test this method. The dataset undergoes pre-processing and dimensionality reduction using PCA, then K-means clustering is applied. The clustering results are evaluated using the Silhouette Score and Davies-Bouldin Index. The implementation is carried out in the Google Colab environment to utilize cloud computing resources. The results show that dimensionality reduction using PCA significantly reduces computational complexity and improves clustering quality. This method is effective in clustering big data, making it an efficient solution for data clustering in cloud computing architecture.
Advanced tourist arrival forecasting: a synergistic approach using LSTM, Hilbert-Huang transform, and random forest Mukhtar, Harun; Remli, Muhammad Akmal; Mohamad, Mohd Saberi; Wan Salihin Wong, Khairul Nizar Syazwan; Ridhollah, Farhan; Deprizon, Deprizon; Soni, Soni; Lisman, Muhammad; Amran, Hasanatul Fu'adah; Sunanto, Sunanto; Ismanto, Edi
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 1: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i1.pp517-526

Abstract

An advanced synergistic approach for forecasting tourist arrivals is presented, integrating long short-term memory (LSTM), Hilbert-Huang transform (HHT), and random forest (RF). LSTM is leveraged for its capability to capture long-term dependencies in sequential data. Additional data from Google Trends (GT) is processed with HHT for feature extraction, followed by feature selection using the RF algorithm. The combined HHT-RF-LSTM model delivers highly accurate forecasts. Evaluation employs regression analysis with metrics such as root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE), highlighting the effectiveness of this innovative approach in predicting tourist arrivals. This methodology provides a robust framework for handling limited datasets and improving forecast reliability. By incorporating diverse data sources and advanced preprocessing techniques, the model enhances prediction performance, demonstrating the strong performance of RF in feature selection.
Co-Authors ., Farid Hasfindra Abdul Ghofur Addarisalam, Alif Al Amien, Januar Aldi, M Tri Alfaizun Nur Alfidin Alfanico, Febrian Alfian, Haris Ali, Zurrahman Muhammad Alris Gusnanda Amin Hariyanto Aminuyati Amran, Hasanatul Fu'adah Amran, Hasanatul Fuadah Apriansyah Apriansyah Arkan, M Alif Arviero L Tobing, Yandi Aryanto, Eggy Asrul Abdurrahim, Abulkhair Auralia Rilda, Azzahra Awaluddin Ayodya Putri Baidarus Bayu Anugerah Putra Benu, M. Rajib Owiendra Br Bangun, Elsi Titasari Budi Arham Chan, Ridzky Dani Harlian Daniel Adi Putra Sitorus Danillo, Amadel Daud, Kauthar Mohd Deprizon, Deprizon Dermawan, Aldi Desti Mualfah Dhea Dahliana Amanda Diah Angraina Fitri Diah Angraini Putri Dian Utami Dinia Putri doni, Rhoma Durades, M.Azri Dwi Pramaditya, Ilham Edi Ismanto Edo Arribe, Edo Efry Hady Nata Eka Putra Evans Fuad Fadly Gunawan Fakhira Frisya Ramadhani Fatchiyah Maharani, Masti Fatma, Yulia Fatma, Yulia Febby Apri Wenando Fitri Handayani Fitri Handayani Fitria Aini, Fitria Fitriani, Aisyah Fu’adah Amran, Hasanatul Gunawan, Rahmad Gusnanda, Alris Hadi Nasbey Hafid, Afdhil Hafsari, Rizka Hanum Salsabila Hardian_Putra, Saddam Haris, Aidil Hartanto, Fizhra Dwi Putra Hasanatul Fu'adah Amran Hasanuddin Hasanuddin Hasanuddin Hasanuddin, Hasanuddin, Hayami, Regiolina Hendri, Yusriadi Herlandy, Pratama Benny Ilham Dwi Pramaditya Indra Saputra Irawan, Eldi Januar Al Amien Januar Al Amien Januar Al Amin Jihan Aulia Jum’atul Zikri Ken Rio Agizki Khusnul Hanafi Khusyaini, Ilham Kultum, Fi Ardhi Kurniawan, Muhammad Irgi Lisman, Muhammad Lorenza, Dina Lutfi Mz, Al Agib M Arif Rucyat M Djodi Andikarama Maisyarah, Adila Marcelino, Ananda Mardiya, Ainul Mas’yuri, Dhina Nurriska Maulana, Ade Irvan Medikawaty Taufiq, Reny Mirano, M. Fitter Mohamad, Mohd Saberi Muchtadi, Bill Fikra Muhammad Abdul Al Aziz Muhammad Fithra Muhammad Ilham Akbar Muhammad Rifaldo Muhammad Taufik Munanda, Rizka Muzahaffar, Fatih Al Nengsih, Rafni Yulia NUR FADILAH Nuradlin Syafini Nurwijayanti O.K Saddam Hussein Okta Tri Antoni Permadi Permadi Peter Wijaya, Peter Pradipa, Raditya Prasasti, Aditia Prastiwi, Adila Pramudiah Prastiwi, Adila Pramudiah Pratiwi Gasril Prayoga, Yuda Putra, Afyuadri Rahma Dayani, Lilian Rahmad Firdaus Rahmad Firdaus Rahman Septiadi Rahman, Rayhan Aulia Rahmawilda, Rahmawilda Rahmayana, Elsa Rama Putra Ramanda, Yuki Rayhan, Aqeel Refni Wahyuni Remli, Muhammad Akmal Reny Medikawati Taufik Revalino, Aqil Rhoma doni Ricinur Ricinur Rico Apriandika Ridhollah, Farhan Rizki Yasin Rizki, Rafi Hamdan Royan Choiro Yahya Saputra Sy, Yandiko Saputra Weisdiyanto, Wahyu Septiana Srinandini Shafwan, Affif Dzaky Silvia Elki Putri Soni Sri Wahyuni Sunanto Sunanto Suryanti, Anggi Aprilia Syafutri, Trimaiyuza Maulina Syahril Syahril Syahril Syahrul R, Syahrul Taufiq, Reny Medikawati Tiara Putri, Rahma Triana Dahar, Ulya Trimuawasih, Diana Unik, Mitra Vanama, Melsa W. M. Azim Wan Salihin Wong, Khairul Nizar Syazwan Wen Jia Wide Mulyana Yordan, Gibril Yos Hendra, Yos Yoze Rizki Yulia Fatma Yulia Fatma Yusoff, Nooraini