Article Per Year (5 Year)
p-Index From 2021 - 2026
5.052
P-Index
This Author published in this journals
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 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
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

Published : 45 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 45 Documents
Search

 1   2   3   4   5 
A comparative study of machine learning methods for drug type classification Tejawati, Andi; Suprihanto, Didit; Ery Burhandenny, Aji; Saipul, Saipul; Puspitasari, Novianti; Septiarini, Anindita
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9477

Abstract

Drugs, commonly called narcotics, are dangerous substances that, if consumed excessively, can result in addiction and even death. Drug abuse in Indonesia has reached a concerning stage. In 2017, the National Narcotics Agency detected 46,537 drug-related incidents, including methamphetamine, marijuana, and ecstasy. There are 4 types of substances that can affect drug users, such as hallucinogens, depressants, opioids, and stimulants. A machine learning approach can detect these substances using user symptom data as input. This study uses six different methods in classifying, including decision tree, C.45, K-nearest neighbor (KNN), random forest, and support vector machine (SVM). The dataset comprises 144 data and 21 attributes based on the user's symptoms. The evaluation method in this study uses cross-validation with K-fold values of 5 and 10 and uses three parameters: precision, recall, and accuracy. KNN yields the most optimal results by using K=1 and K-fold 10 in the Euclidean and Minkowski types. The model achieves precision, recall, and accuracy of 91.9%, 91.7%, and 91.67%, respectively.
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.
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 Fadli Suandi Fahrul Yamani Fairil Anwar Fajar Fatimah Faza Alameka Fernando Elda Pati Ferry Miechel Lubis Firdaus, Muhammad Bambang Friendy Prakoso Hairah, Ummul Hairah, Ummul 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 Ramadhan, Khefyn Rantetana, Stevie Falentino Renol Sulle Richard Giovanni Ardie Wong Riyayatsyah, Riyayatsyah Rizqi Saputra 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