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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) International Journal of Informatics and Communication Technology (IJ-ICT) Bulletin of Electrical Engineering and Informatics Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SemanTIK : Teknik Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab SISFOTENIKA JURNAL INTEGRASI JRST (Jurnal Riset Sains dan Teknologi) JURNAL REKAYASA TEKNOLOGI INFORMASI Jurnal Teknoinfo ILKOM Jurnal Ilmiah Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) METIK JURNAL Jurnal Informatika dan Rekayasa Elektronik Sains, Aplikasi, Komputasi dan Teknologi Informasi TEPIAN Jurnal Teknologi Informatika dan Komputer Jurnal Sistem Informasi dan Sistem Komputer Jurnal Teknik Informatika (JUTIF) Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Inovasi Teknologi Masyarakat Journal of Soft Computing Exploration
Tejawati, Andi
Program Studi Informatika, Fakultas Teknik, Universitas Mulawarman, Indonesia
Aerospace Engineering Automotive Engineering 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 Electrical & Electronics Engineering Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences Other

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COMBINATION OF WP AND TOPSIS METHODS IN A DECISION SUPPORT SYSTEM FOR WATERMELON SEED RECOMMENDATION Tejawati, Andi; Puspitasari, Novianti; Pasorong, Hillary Bella; Masa, Amin Padmo Azam
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2317

Abstract

Watermelon is a horticultural plant that can be cultivated by the wider community with adequate profits. In Indonesia, watermelon production is still unable to meet the huge market demand and has not been able to be met by local watermelon-producing areas. One of the reasons why watermelon production is insufficient is because the fruit is easily damaged due to inappropriate watermelon seeds. The right watermelon seeds can be selected using a Decision Support System. This study uses two combination methods, namely Weighted Product (WP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to be applied in the Decision Support System for watermelon seed recommendations. The WP method is used to determine the weight of the criteria, while TOPSIS is used to determine the order of watermelon seed recommendations. The data used in this study were twenty alternative watermelon seeds with five criteria, namely land recommendations, yield potential, fruit weight, harvest age, and disease resistance. Of the five criteria determined by the WP method, the largest criterion value is in the land recommendation. The results of the implementation with both methods produced recommendations for watermelon seeds, with the first ranking result being the F1 Series (3n) watermelon seeds with a preference value of 0.85442, and black box testing showed that this system was able to provide recommendations for quality watermelon seeds according to their functionality based on the application of the WP and TOPSIS methods.
Medicinal Plants Recommendation System using ROC and MOORA Widians, Joan Angelina; Tejawati, Andi; Yuniarti, Wenty Dwi
TEPIAN Vol. 5 No. 2 (2024): June 2024
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v5i2.3019

Abstract

Kalimantan has extraordinary biodiversity, including medicinal plants. Medicinal plants are a type of plant that certain parts, such as roots, leaves, bark, stems, and the results of their excretions. However, people sometimes need help choosing plants that suit their needs because of the many types of medicinal plants and the need for knowledge regarding their use. Decision support systems (DSS) combine computer capabilities with data processing or manipulation that utilizes unstructured models or solution rules. Furthermore, the method of documenting knowledge of traditional medicine is through the media of information systems. This system helps select medicinal plants according to user needs. This research developed a DSS using Rank Order Centroid (ROC) and Multi-Objective Optimization by Ratio Analysis (MOORA) methods to select medicinal plants for fungal and skin infections, including Furuncles, Tinea corporis, Tinea versicolor, and Acne. ROC method for determining criteria weight values. This research has four criteria: plant part, processing method, use method, and habitus. Determining recommendations for alternative ranking results using the MOORA method. This study aims to help the public get recommendations for medicinal plants in human skin disease treatment. This study aims to increase the preservation of biodiversity, particularly sustainable medicinal plants in the tropical rainforest of East Kalimantan.
Metode Fuzzy Multiple Attribute Decision Making (FMADM) dengan Weighted Product (WP) dalam Menentukan Varietas Bawang Merah Rantetana, Stevie Falentino; Puspitasari, Novianti; Tejawati, Andi
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 9, No 3 (2025): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v9i3.21948

Abstract

Bawang merah atau Allium cepa.L dalam bahasa latin merupakan tanaman rempah yang menjadi salah satu komoditas pertanian di Indonesia. Rempah ini banyak digunakan sebagai bahan masakan yang menyebabkan kebutuhan bawang merah di masyarakat sangat besar. Salah satu cara untuk memenuhi kebutuhan bawang merah dapat dilakukan dengan membudidayakan bawang merah secara mandiri. Namun, banyaknya varietas bawang merah menjadikan masyarakat bingung untuk memilih varietas yang sesuai. Penelitian ini mengembangkan metode Fuzzy Multiple Attribute Decision Making (FMADM) dengan pendekatan Weighted Product (WP) untuk membantu dalam proses pengambilan keputusan pemilihan varietas bawang merah yang paling sesuai berdasarkan beberapa kriteria. Kriteria yang digunakan meliputi susut bobot, umur panen, daya simpan, jumlah umbi, dan potensi hasil. Hasil penerapan metode FMADM WP menunjukkan bahwa dari sebelas varietas yang ada, varietas TSS Agrihort 1 sebagai varietas terbaik dengan nilai preferensi  0,1556. Dari hasil tersebut terlihat bahwa metode ini dapat menjadi alat bantu yang efektif dalam mendukung pengambilan keputusan varietas bawang merah yang optimal.
Comparison of ResNet50, ResNet101, and ResNet152 Architectures in Image-Based Rice Leaf Disease Classification Ardi Setyiawan; Septiarini, Anindita; Andi Tejawati
Jurnal Teknologi Informatika dan Komputer Vol. 12 No. 1 (2026): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v12i1.3289

Abstract

Rice leaf diseases are one of the main threat that can reduce rice crop productivity especially if they are not detected at an early stage. Conventional disease identification still has limitations because it relies on visual observation and the experience of farmers. Therefore, this study proposes a rice leaf disease classification approach based on digital images using deep learning methods. This study aims to compare the performance of three Residual Network architectures, namely ResNet50, ResNet101, and ResNet152. The dataset used was collected from three public Kaggle datasets, consisting 7.322 images divided into four classes (healthy, hispa, sheath blight, and brown spot). The dataset was split into training, validation, and testing sets with a ratio of 70:20:10 and processed through image preprocessing and data augmentation. All models were trained using a transfer learning approach with the same training configuration to ensure a fair comparison. Model performance was evaluated with the test sets using loss, accuracy, and confusion matrix analysis. The experimental results show that ResNet101 achieved the best performance with a loss value of 0,0146 and an accuracy of 0,9973. Followed by ResNet50 with an accuracy of 0,9918, and ResNet152 with an accuracy of 0,9837. These results indicate that ResNet101 provides the best balance between network depth and classification performance.
Multiclass SVM with Kernel Optimization for Schizophrenia Subtype Classification Using Clinical Symptom Records Rohman, Reisa Maulidya; Septiarini, Anindita; Tejawati, Andi
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.15926

Abstract

Schizophrenia is a mental disorder that affects about 0.3% of the world population. It is characterized by a wide range of symptoms that form several subtypes. Overlapping symptoms and subjective clinical assessments may reduce consistency and make subtype classification challenging. Machine learning algorithms that use patients’ medical records offer a potentially objective approach for subtype classification. This study aims to classify four schizophrenia subtypes: paranoid, catatonic, undifferentiated, and residual, based on subtype labels recorded in the hospital using a multiclass SVM approach with kernel optimization. The dataset consists of 218 medical records of schizophrenia patients with 25 binary symptom variables used as input features. SVM was trained using two multiclass approaches, namely OAO and OAA. Evaluation was performed using five-fold stratified cross-validation. Performance was calculated using accuracy, macro-precision, macro-recall, and macro F1-score. Optimal performance was achieved using the OAA approach with an RBF kernel at C = 10 and gamma = 0.1. This configuration achieved an accuracy, macro-precision, macro-recall, and macro F1-score of 0.89, 0.90, 0.86, and 0.87, respectively. These results show that the multiclass approach, kernel functions, and parameter configuration influence classification performance. The proposed model may serve as a screening or decision-support tool to assist subtype identification based on clinical symptom records.  
Penentuan Prioritas Kesejahteraan Keluarga Menggunakan Metode the Extended Promethee II Wati, Masna; Lubis, Ferry Miechel; Tejawati, Andi
ILKOM Jurnal Ilmiah Vol 12, No 1 (2020)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v12i1.528.71-80

Abstract

Indonesia is a developing country in which poverty is one of the problems faced by each province. The government continues to strive to overcome this problem of poverty to be able to create conditions for a prosperous society. The government's efforts to poverty alleviation are by providing some assistance programs. Therefore, it is necessary to build a decision support system that is useful to help the government to make a decision. This decision support system applies the EXPROM II (The Extended Promethee II) method with the weight of objective criteria. There are 15 criteria used based on SUSENAS data from the Statistics Indonesia of East Kalimantan Province. This research resulted in a decision support system that can give priority order of the level of family welfare so that it can be considered or referred by the local government or related agencies in distributing assistance to the society.
Comparative of YOLOv5 and YOLOv8 for rice leaf disease detection on diverse image datasets Fadhillah, Muhammad Nandaarjuna; Septiarini, Anindita; Hamdani; Rajiansyah; Andi Tejawati
Journal of Soft Computing Exploration Vol. 7 No. 1 (2026): March 2026
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v7i1.19

Abstract

Rice (Oryza sativa) is Indonesia’s primary food crop, yet its productivity is often threatened by leaf diseases such as Brownspot, Hispa, and Sheath Blight. To address the limitations of manual inspection, this study proposes an automated detection and classification framework based on deep learning, with a comparative evaluation of the YOLOv5 and YOLOv8 models. This study is novel in that it assesses the robustness of models across a variety of data sources, such as a public dataset collected under controlled conditions and a private dataset collected in the field that replicates real-world agricultural contexts. The experimental results suggest that YOLOv8 consistently outperforms YOLOv5 in a variety of evaluation metrics. YOLOv8 performed best on the private dataset, with a precision of 0.907, recall of 0.886, F1-score of 0.896, Intersection over Union (IoU) of 0.71, and mAP50 of 0.924 under the 90:5:5 data split configuration. It shows that it can detect things well even in difficult field conditions. Both models performed about the same on the public dataset; however, YOLOv8 was better at finding objects, as shown by higher mAP50–95 values. Both models also did a great job of classifying; however, YOLOv8 was better at generalising across different dataset distributions. These results demonstrate that YOLOv8, which operates without anchors, is a superior and more dependable method for the real-time detection of rice leaf disease. This study offers pragmatic insights for implementing advanced computer vision models in precision agriculture systems, particularly in resource-constrained, dynamic agricultural environments.
Co-Authors Achmad, Rayhan Zidane Ade Chrisvitandy Ahmad Wahbi Fadillah Alameka, Faza Anam, M Khairul Andi Azza Az-Zahra Andi Muhammad Redha Putra Hanafiah Anindita Septiarini, Anindita Anjas, Andi Anton Prafanto Arba, Muhammad Hendra Ardi Setyiawan Arief Hidayat Bambang Cahyono Budiman, Edy Budiman, Edy Damayanti, Elok Didit Suprihanto, Didit Eddy Kurniawan Pradana Eka Priyatna, Surya Ery Burhandenny, Aji Fadhillah, Muhammad Nandaarjuna Fadli Suandi Fahrul Yamani Fairil Anwar Fajar Fatimah Faza Alameka Fernando Elda Pati Firdaus, Muhammad Bambang Friendy Prakoso Hairah, Ummul Hairah, Ummul Hamdani Hamdani Hamdani Hanif Aulia Hasman, Firnawan Azhari Heni Sulastri Herman Santoso Pakpahan indrajit, Indrajit Irfan Putra Pratama Irsyad, Akhmad Joan Angelina Widians, Joan Angelina Kamila, Vina Zahratun Lathifah Lathifah Lathifah Lathifah Lubis, Ferry Miechel M Syauqi Hafizh Masa, Amin Padmo Azam Masna Wati Medi Taruk Muhammad Bambang Firdaus Muhammad Budi Saputra Muhammad Nopri Fauzi Muhammad Nur Ihwan Nariza Wanti Wulan Sari Novianti Puspitasari Pasorong, Hillary Bella Pohny Pohny Puspita Octafiani Puspitasari, Novianti Rajiansyah Ramadhan, Khefyn Rantetana, Stevie Falentino Renol Sulle Richard Giovanni Ardie Wong Riyayatsyah, Riyayatsyah Rizqi Saputra Rohman, Reisa Maulidya Rondongalo Rismawati Rosmasari Rosmasari, Rosmasari Saipul, Saipul Setyadi, Hario Jati Sofiansyah Fadli Sukma Dewi Hardi Yanti Syahbana, Syarif Nur Taruk, Medi Wahyudianto, Mochamad Rizky Wahyudin Wahyudin Waksito, Alan Zulfikar Wardhana, Reza Wati, Masna Wenty Dwi Yuniarti, Wenty Dwi Widians, Joan Angelina Zainal Arifin Zainal Arifin