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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Buana Informatika Teknosains: Media Informasi Sains dan Teknologi Jurnal Teknologi Informasi dan Ilmu Komputer SIGMA: Jurnal Pendidikan Matematika AlphaMath: Journal of Mathematics Education JOIV : International Journal on Informatics Visualization Al Ishlah Jurnal Pendidikan Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JPM (Jurnal Pemberdayaan Masyarakat) Faktor Exacta Jurnal Penjaminan Mutu JITK (Jurnal Ilmu Pengetahuan dan Komputer) JMM (Jurnal Masyarakat Mandiri) JTAM (Jurnal Teori dan Aplikasi Matematika) CARADDE: Jurnal Pengabdian Kepada Masyarakat Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JURNAL MathEdu (Mathematic Education Journal) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) GERVASI: Jurnal Pengabdian kepada Masyarakat TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Sistem Informasi dan Informatika (SIMIKA) Reswara: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Unri Conference Series: Community Engagement Jurnal Dedikasi International Journal of Electronics and Communications Systems Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat Online Learning in Educational Research Seminar Nasional Pengabdian Kepada Masyarakat Catimore: Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Edutic : Pendidikan dan Informatika Internet of Things and Artificial Intelligence Journal Jurnal Penjaminan Mutu Indonesian Journal of Fundamental Sciences IPTEK: Jurnal Hasil Pengabdian kepada Masyarakat Teknovokasi : Jurnal Pengabdian Masyarakat Vokatek : Jurnal Pengabdian Masyarakat Information Technology Education Journal Pengabdian Jurnal Abdimas Journal of Embedded Systems, Security and Intelligent Systems Ininnawa: Jurnal Pengabdian Masyarakat Journal of Vocational, Informatics and Computer Education Jurnal Kemitraan Responsif untuk Aksi Inovatif dan Pengabdian Masyarakat Jurnal Sipakatau Jurnal Ilmu Pengetahuan dan Teknologi Bagi Masyarakat Jurnal MediaTIK Mekongga: Jurnal Pengabdian Masyarakat Media Elektrik Malaqbiq : Jurnal Pengabdian kepada Masyarakat. Sasambo: Jurnal Abdimas (Journal of Community Service) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Journal of Applied Artificial Intelligence in Education
Andi Baso Kaswar Andi Baso Kaswar
Universitas Negeri Makassar
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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
A NEW MODEL FOR HYDROPONIC LETTUCE NUTRITION ADAPTIVE CONTROL SYSTEM BASED ON FUZZY LOGIC SUGENO METHOD USING ESP32 Andi Baso Kaswar; Ridwan Daud Mahande; Jasruddin Daud Malago
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 2 (2023): JUTIF Volume 4, Number 2, April 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.2.626

Abstract

In the last few years, the terms Smart Agriculture, Smart Farming, Urban Farming, or Precision Farming have been increasingly recognized and growing rapidly. Hydroponics is one part that is currently a trend, both in industrial or household scale businesses and hobbies. One of the most important things to consider in maintaining the quality of hydroponic plant growth is the concentration of nutrients in the water. A series of studies have been conducted to improve the quality of hydroponic plants. However, the developments that have been carried out have not focused on optimal nutritional control. The previous hydroponic plant nutrition control system still used conventional methods, namely the use of a rule base with firm values ​​, and did not consider the quantity and quality of water. Therefore, this study proposes a new model for an adaptive control system for hydroponic lettuce nutrition based on the Fuzzy Logic Sugeno method using ESP32. The fuzzy logic Sugeno method is used to create a new model of the inference system for determining the amount of nutrient dosage based on supporting data obtained from sensors installed on hydroponic growing media. Compared with the conventional method, the resulting test results show that the proposed method can adapt the amount of added nutrients, provide optimal nutrient addition output, and prevent excess nutrient additions that can potentially accumulate toxic ions in water that degrade water quality.
CLASSIFICATION OF TOMATO QUALITY BASED ON COLOR FEATURES AND SKIN CHARACTERISTICS USING IMAGE PROCESSING BASED ARTIFICIAL NEURAL NETWORK Agung, Andi Sadri; SR, Amin Farid Dirgantara; Hersyam, Muh Syachrul; Kaswar, Andi Baso; Andayani, Dyah Darma
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 5 (2023): JUTIF Volume 4, Number 5, October 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.5.730

Abstract

Tomato (Solanum Lycopersicum) is a plantation commodity in Indonesia with a production rate that tends to increase every year. With a high economic value, maintenance is important so that the quality is getting better. The problems that arise at this time are related to the determination of the quality of tomatoes which is still done manually and depends on humans so classification using technology is considered important to be developed. Previously there has been researching related to the classification of tomatoes. However, accuracy and computation time still need to be improved. Therefore, in this research, a method of classification of tomatoes was carried out using Artificial Neural Network (ANN) Backpropagation algorithm by utilizing color features and skin characteristics based on image processing. This research followed several stages, from acquiring 300 tomato images with 3 class levels to the classification process using ANN Backpropagation. Several training scenarios and tests were conducted to select the feature combined with the highest accuracy and fastest computation time. The combination of 3 best features used is RGB color feature with shape and texture features as skin characteristic parameters. Based on training results with 210 training images, an accuracy of 100% was obtained with a computation time of 2.58 seconds per image. While test results with 90 test images, accuracy reaches 95.5% with a computing time of 1.39 seconds per image. So it can be concluded that the method used has gone well in classifying tomato image quality based on color features and skin characteristics.
MATURITY CLASSIFICATION SYSTEM OF TOMATO BASED ON RGB COLOR FEATURES USING BACKPROPAGATION ARTIFICIAL NEURAL NETWORK METHOD Massie, Gary Jeremi; Pratama, Azir Zuldani; Sakira, Tiara Putri; Kaswar, Andi Baso; Andayani, Dyah Darma
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.732

Abstract

Determining the ripeness level of tomatoes, for now, is still done manually (conventional), and in general, determining the ripeness of tomatoes using the manual method often gets inconsistent results due to differences in everyone's perception so in determining ripe or not ripe tomatoes to be not very accurate. There have been various previous studies that have been conducted, especially in terms of classifying maturity levels, but from these studies, the level of accuracy achieved is relatively low. Therefore, the researcher proposes research on Tomato Fruit Maturity Classification System Based on RGB Color Features Using the Backpropagation Neural Network Method. This research consists of the image acquisition stage, the preprocessing stage, the image segmentation stage including performing morphological operations, the RGB feature extraction stage, and the last stage is conducting Image Classification using Backpropagation Neural Networks. From the results of the training phase, the resulting computational time is 87,735 seconds with an overall accuracy rate of 99.04%. And based on the results of the testing phase, the architecture of the backpropagation neural network that has been built can accurately classify the ripeness level of tomatoes, from a total of 90 test images, with an accuracy of 98.88% obtained and a more efficient computational time of 30.390 seconds. This can help farmers in harvesting tomatoes.
CLASSIFICATION OF THE LEVEL OF SUGAR CONTENT IN PAPAYA FRUIT BASED ON COLOR FEATURES USING ARTIFICIAL NEURAL NETWORK Nurfitri, Andi Aisyah; Kaparang, Adam Indra; Hidayat, Muh. Taufik; Kaswar, Andi Baso; Andayani, Dyah Darma
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.733

Abstract

Papaya (Carica papaya L) is consumed by many people because it is beneficial for health. Along with increasing consumption or enthusiasts of papaya, the quality of papaya needs to be considered. One of the determining factors of the quality of papaya is its physical characteristics, which can be seen from its color, shape, and texture. Papaya of good quality has a delicious and sweet taste. The sweet taste of papaya is certainly influenced by the sugar content contained in it. However, to determine the sugar content in papaya is only done by human assessment based on its physical characteristics, this assessment is often less accurate. With a system that can determine the sugar content in papaya, it will make it easier for farmers to sort papaya fruit. Therefore, in this study, it is proposed to classify the level of sugar content in papaya based on color features using an Artificial Neural Network. The proposed method consists of 5 stages, namely, image acquisition, preprocessing, segmentation with the Otsu method, morphological operations, and classification with artificial neural networks. The number of papaya datasets used is 300 images which are divided into 3 classes, low class, medium class, and tal class. Based on the results of the tests that have been carried out, an accuracy of 92.85% is obtained for the training data, and for the test data, an accuracy of 100% is obtained. These results indicate that the proposed method can classify the level of sugar content in papaya fruit accurately.
CLASSIFICATION OF RICE QUALITY LEVELS BASED ON COLOR AND SHAPE FEATURES USING ARTIFICIAL NEURAL NETWORK BASED ON DIGITAL IMAGE PROCESSING Asnidar, Asnidar; Perdana, Am Akbar Mabrur; Ilham, Muhammad Ryan; Kaswar, Andi Baso; Andayani, Dyah Darma
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.734

Abstract

Rice is the staple food of most Indonesians. In identifying the quality of rice, it can be seen from physical characteristics such as the color and shape of rice, because these characteristics can make an object can be identified properly and clearly. In general, what is done in determining the quality of rice by looking at its color and shape. But usually the human eye in identifying objects is sometimes less accurate which is influenced by several factors, such as age. So, several studies were conducted that tried to solve the problem by using digital image processing. However, the accuracy results obtained are still not accurate, because the datasets used in the previous study were relatively small, namely around 80 images, although the average level of accuracy obtained was quite high, but the number of datasets used was very small so that the level of accuracy was still inaccurate. Therefore, in this study, it is proposed that the title of classification of rice quality levels using JST based on digital image processing which divides rice into 3 classifications, namely, good, good enough, and not good where in this study using 330 digital images to produce a more accurate level of accuracy. In this study, there are several stages, namely, image retrieval, preprocessing, segmentation, morphological, feature extraction, and classification using artificial neural networks. Based on the research conducted, training accuracy was produced with an average accuracy of 98,75% while the test accuracy was produced with an average accuracy of 98,89%.
Carrot Quality Classification Based on Color and Texture Features Using Artificial Neural Network Method Idris, Muh Gimnastiar; Fauzi, A. Arfan; Syasikirani. N, Adelia; Kaswar, Andi Baso
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.1401

Abstract

Carrots are popular vegetable plants that are usually consumed by the public. Determination of quality using the visual of human eye is considered to have many shortcomings. In previous studies, the carrot classification process had been carried out using a certain method. However, the level of accuracy resulting from several previous studies is still lacking because the processes and methods used are considered to be inaccurate, so innovation is needed by using processes and methods that are more precise to obtain classification results with a better level of accuracy. Therefore, this research proposes a classification of carrot quality based on color and texture features using an artificial neural network method. The proposed method consists of 6 stages, namely image acquisition, preprocessing, segmentation, morphological operations, feature extraction, and classification using artificial neural networks. In this study, quality is divided into three classes, namely feasible, less feasible, and not feasible using 300 carrot image datasets. The results obtained in the testing process obtained an accuracy of 100%, a misclassification error of 0%, and a computation time of up to 55 seconds. Based on the test results it can be seen that the proposed method can classify the quality of carrots accurately.
CLASSIFICATION OF SUGAR LEVELS IN BANANA FRUIT BASED ON COLOR FEATURES USING DIGITAL IMAGE PROCESSING-BASED ARTIFICIAL NEURAL NETWORKS S, Mushawwir; Burhan, Rafli Ananta; Yuliarni, Tarisa; Kaswar, Andi Baso; Andayani, Dyah Darma
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.1420

Abstract

Bananas are a fruit that has many benefits for human health, because bananas contain a source of vitamins, minerals and carbohydrates. Bananas are a fruit that is often consumed by Indonesian people because of their sweet taste. With this sweet taste, of course bananas have quite high sugar levels, so diabetes sufferers must pay attention to this when choosing bananas. The level of sugar content in bananas can be distinguished by looking at the ripeness of the fruit. To differentiate between them, of course, we use human vision, but human observation also has weaknesses and errors can occur in the process, whether due to lack of lighting, visual impairment, or age. Therefore, this study proposes a classification of the level of sugar content in bananas in the RGB color space using artificial neural networks (ANN). The proposed method consists of 6 stages, namely image acquisition, preprocessing, segmentation, morphological operations, RGB feature extraction, and classification stage. In this study, 300 samples of banana fruit images were used. 210 datasets will be used for training and 90 datasets for testing. The dataset is divided into 3 classes, namely low sugar content, medium sugar content, and high sugar content. Based on the test results that have been carried out, the accuracy of the classification results is 97.78%, the misclassification is 2.22%, and the computing time is 375 seconds. These results show that the proposed method can accurately classify the level of sugar content in bananas.
Co-Authors A. Farha Adella A. Muhammad Idkhan A. Mutahharah A. Mutahharah Mutahharah A.Farha Adella Abd. Rahman Patta Abdul Muis Mappalotteng Abdul Wahid Adiba, Fhatiah Afdhaliyah, Mukhlishah Afyan, Nurbaitul Aglaia, Alifya Nuraisyar Agung, Andi Sadri Agus Zainal Arifin Agus Zainal Arifin Agustinus Suria Darme Ahmad Adzan Lain Ahmad Fudhail  Majid Ahmad Khan, Sardar Faroq Ahmad Mustofa Hadi Ahmad Mustofa Hadi Ainun Zahra Adistia Akbar, Trisakti Aksa, Muhammad Alfian Firlansyah Ananta Dwi Prayoga Alwy Andi Ahmad Taufiq Andi Akram Nur Risal Andi Alamsyah Rivai Andi Fitri Novianti Andi Nurul Izzah Andi Rosman N Andi Tenriola Anggy Heriyanti Anggy Heriyanti Annajmi Rauf Anny Yuniarti Aprilianti Nirmala S Aqsha, Ismail Aras, Muh Riski Farukhi Arifky, Reza Arinanda Alviansyah Arliandy, Arliandy Arsyad, Meisaraswaty Arya Yudhi Wijaya Arya Yudhi Wijaya Aryadi Nurfalaq Ashadi, Ninik Rahayu Asmi Ulfiah Asnidar Asnidar Asrofi, Muhammad Ghufran Astuti, Ninik Aswar Aswar Aulia, Magfirah Awalia, Nur Ayu Futri Azis, Putri Alysia Azis, Salsabila Bantun, Suharsono Bugdady, Andi Jaedil Bukhari Naufal Nur A.G Burhan, Rafli Ananta Chairati, Chairati Cyahrani Wulan Purnama Cyahrani Wulan Purnama Rasyid Darma Andayani, Dyah Darme, Agustinus Suria Della Fadhilatunisa Desitha Cahya Dewi Fatmarani Surianto Dhanendra, Fadhil Dina Salam, Fitria Nur Dirawan, Gufran Darma Edy, Marwan Ramdhany Elva Amalia Elva Amalia Eman Wahyudi Kasim Eriyani, Nindy Sri Fachriansyah, Zaky Farid, Muhammad Miftah Farros Taufiqurrahman Fathahillah Fathahillah Fauzi, A. Arfan Fazli Arif Fhatiah Adiba Fhatiah Adiba Fhatiah Adiba Hafidz Muhtar Hanum Zalsabilah Idham Hartanto Tantriawan Heriyanti, Anggy Herman Hermansyah Hermansyah Hersyam, Muh Syachrul Hidayat, Muh. Taufik Ibnu Fikrie Syahputra Idkhan, A. Muhammad Idkhan, Andi Muhammad Idris, Muh Gimnastiar Ihlasul Amal Ikra Ain Fahwa Ikra Ain Fahwa Ilham, Muh Ilham, Muhammad Ryan Ilyas, Muh. Imran, Al Indri Pratiwi Ramadhani Intam, Reski Nurul Jariah S Irwansyah Suwahyu ISHAK Israwati Hamsar Iwan Suhardi Jamaluddin, Bunga Mawar Jamila Jamila Jamila Jariah S.Intam, Rezki Nurul Jasruddin Daud Malago Jayanti Yusmah Sari Jessica Crisfin Lapendy Juliano Nufiansyach Dini Jumadi Mabe Parenreng Jusrawati Jusrawati Jusrawati Kaparang, Adam Indra Kaswar, A Baso Kurnia Prima Putra Kurnia Wahyu Prima Labusab Labusab Labusab Labusab, Labusab Lapendy, Jessica Crisfin Lavicza, Zsolt M. Miftach Fakhri Makmur, Haerunnisya Mappaita, Al Haytsam Marwan Eka Ramdhany Marwan Ramdhany Edy Massie, Gary Jeremi Maulana Muhammad Maulana Muhammad Mawaddah, Arini Ulfa Muammar Muammar Muh Aldhy Fatahillah Muh Devan Fahresi Muh Fuad Zahran Firman Muh Omar Hassan ST Muh. Dirgafa Anugra Rais Muh. Dirgafa Anugrah Rais Muh. Fardika Pratama Putra Muh. Fauzan Arifuddin Muh. Rais Muh. Rasul D Muhammad Agung Muhammad Agung Muhammad Akbar Muhammad Akil, Muhammad Muhammad Atthariq Muhammad Fajar B Muhammad Naim Muhammad Nur Yusri Maulidin Yusuf Muhammad Nur Yusri Maulidin Yusuf Muhammad Yahya Muhiddin Palennari Muhira Muhira Muhtar, Hafidz Mukhtar Mukhtar Mulia, Musda Rida Muliaty Yantahin Musdar, Devi Miftahul Jannah Mustari Lamada Mutahharah, A Naim, Muhammad Nasrullah, Asmaul Husnah NFH, Alifya NIRMALA, PUTRI Nirsal Nur Anny S. Taufieq Nur Fadillah Bustamin Nur Inayah Yusuf Nurfalaq, Aryadi Nurfitri, Andi Aisyah Nurhidayat Nurhidayat Nurhikma Nurhikma Nurhikma Nurjannah Nurjannah Nurjannah Nurjannah Nurjannah Nurjannah Nurjannah Nurjannah Nurul Amanda Pratiwi Hasbullah Nurul Isra Humaira B Nurul Istiqamah Qalbi Nurul Izzah Dwi Nurul Izzah Dwi Nurdinah Patongai, Dian Dwi Putri Ulan Sari Perdana, Am Akbar Mabrur Pramudya Asoka Syukur Pratama, Azir Zuldani Putri Ramdani R, Muh Raflyawan R, Ranir Aftar Ranggareksa, Andi Ranir Atfar R Rapa, Wiwi Resky, Andi Aulia Cahyana Riana T. Mangesa Riana T. Mangesa Ridwan Daud Mahande Ridwansyah Riswansyah , Muh Fikra Junian Rivai, Andi Tenri Ola Riyama Ambarwati Rosidah RR. Ella Evrita Hestiandari Rusli, Risvan S, Mushawwir Sahribulan Sahribulan Saiful Bahri Musa Sakira, Tiara Putri Sam, Muh Hadal Ali Sanatang Saparuddin Saparuddin Saparuddin Saparuddin Saprina Mamase Saputra, Nikola Sartika Sari Sartika Sari Sasmita Sasmita Sasmita SATRIYAS ILYAS Silvia Andriani Soeharto Soeharto SR, Amin Farid Dirgantara Sri Rahayu St. Fatmah Hiola Suharsono Bantun Suhartono, Suhartono Supria Supria Surianto, Dewi Fatmawati Susiana Sari Syamsuddin Syasikirani. N, Adelia Tenriajeng, Andi Afrah Tenriola, Andi Tri Afirianto, Tri Tsabita Syalza Billa Tsabita Syalza Billa Irawan Umar, Nur Fadhilah Wahda Arfiana AR WAHYUDI Wanda Hamidah Wardani, Ayu Tri Wiwi Rapa WULANDARI Yasser Abd Djawad Yuliarni, Tarisa Yusuf, Zulfatni Zulfikar, Muh. Ihsan