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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 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

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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.
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.
PENGUJIAN USER EXPERIENCE PADA APLIKASI SMART HOME MENGGUNAKAN USE QUESTIONNAIRE Muhammad Bambang Firdaus; Irfan Putra Pratama; Andi Tejawati; M Khairul Anam; Fadli Suandi
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 7 No 1 (2022): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v7i1.1929

Abstract

This research was conducted to design an Android-based Smart Home application user interface that is easily understood by users to control the system and test the User Experience on the smart home application design. The data collection methods used were questionnaires, observations, and literature studies. For the system development method using the Linear Sequential Model / Waterfall Model method. Applications used in making designs and smart home applications include Android Studio, Sublime, Xampp, Adobe Xd, and Adobe Illustrator. The testing methods used include Usability testing and Usability Measurement using the USE Quistionnaire. This research produces a Smart Home application design that can be used by users to easily control the system.
Oil Palm Stem Disease Detection Based on Color Moments and GLCM Texture Features Using Artificial Neural Networks Hamdani, Hamdani; Septiarini, Anindita; Akhmad Syaifudin, Encik; Tejawati, Andi; Zulfariansyah, Muhammad
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5554

Abstract

Oil palm is an essential commodity for the economy; however, basal stem rot caused by Ganoderma boninense poses a significant threat to plantation productivity and long-term vitality. It highlights the importance of early detection of stem disease to facilitate timely intervention and minimize potential economic losses. This study presents an image-based approach to diagnosing oil palm stem maladies, leveraging handcrafted color and texture features within a supervised machine learning framework. The dataset contained 525 images of oil palm stems, of which 205 depicted healthy specimens, and 320 depicted diseased ones. These were captured within their natural environment. Color features were derived by analyzing color moments within the HSV color space, while texture features were extracted from the Grey-Level Co-occurrence Matrix (GLCM). The extracted features were classified employing an Artificial Neural Network (ANN) and were subsequently contrasted with classifiers including Decision Tree, K-Nearest Neighbors, Naive Bayes, and Support Vector Machine. Model performance was evaluated using k-fold cross-validation with k = 5 and k = 10 to ensure the consistency and reliability of the assessment. The experimental results demonstrated that the highest accuracy of 97.52% was achieved when the ANN model was used to classify the integrated color and texture features. The innovative aspect of this research resides in demonstrating that handcrafted features integrated with artificial neural networks can attain high detection accuracy in scenarios with limited data, providing a viable alternative to data-intensive deep learning techniques. This method facilitates a dependable, computer vision-driven early detection system for oil palm stem diseases, thereby promoting sustainable plantation management. 
Klasifikasi Status Gizi Balita Menggunakan Metode K-Nearest Neighbor Syifani, Sarah; Septiarini, Anindita; Taruk, Medi; Wati, Masna; Tejawati, Andi
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 10, No 1 (2026): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v10i1.25641

Abstract

Status gizi balita merupakan indikator penting dalam menilai tingkat kesehatan anak yang dapat diketahui melalui pemeriksaan antropometri. Berdasarkan data Survei Status Gizi Indonesia (SSGI) tahun 2022, prevalensi gizi kurang di Provinsi Kalimantan Timur mencapai 23,9%, khususnya di Kota Samarinda sebesar 25,3%, yang menunjukkan bahwa masalah gizi masih perlu mendapatkan perhatian. Penelitian ini bertujuan untuk mengklasifikasikan status gizi balita menggunakan metode K-Nearest Neighbor (KNN) dengan variasi nilai K dan rumus jarak yang berbeda guna memperoleh performa terbaik. Data yang digunakan merupakan data sekunder dari Puskesmas Pasundan, Kota Samarinda, sebanyak 760 data balita usia 0–60 bulan. Tahapan penelitian meliputi pengumpulan data, perancangan data melalui proses preprocessing (data selection, penanganan outlier, dan transformasi data menggunakan min-max normalization serta encoding), implementasi metode KNN dengan variasi nilai K (1, 3, 5, 7, 9, 11) dan rumus jarak (Euclidean, Manhattan, Minkowski), serta evaluasi model menggunakan Confusion Matrix Multiclass. Berdasarkan hasil pengujian, akurasi tertinggi diperoleh sebesar 89,24% dengan nilai presisi 66,29%, recall 63,70%, dan F1-score 63,12% menggunakan nilai K = 1 dan rumus jarak Euclidean. Hasil ini menunjukkan bahwa metode KNN mampu memberikan performa klasifikasi yang baik dalam menentukan status gizi balita berdasarkan atribut usia, jenis kelamin, berat badan, dan tinggi badan.
Co-Authors Achmad, Rayhan Zidane Ade Chrisvitandy Ahmad Wahbi Fadillah Akhmad Syaifudin, Encik Alameka, Faza Anam, M Khairul Andi Azza Az-Zahra Andi Muhammad Redha Putra Hanafiah Anindita Septiarini, Anindita Anjas, Andi Anton Prafanto Arba, Muhammad Hendra Arief Hidayat Bambang Cahyono Budiman, Edy Budiman, Edy Damayanti, Elok Didit Suprihanto, Didit Eddy Kurniawan Pradana Ery Burhandenny, Aji Fadli Suandi Fahrul Yamani Fairil Anwar Fajar Fatimah Faza Alameka Fernando Elda Pati 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 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 Rohman, Reisa Maulidya Rondongalo Rismawati Rosmasari Rosmasari, Rosmasari Saipul, Saipul Setyadi, Hario Jati Sofiansyah Fadli Sukma Dewi Hardi Yanti Surya Eka Priyatna Syahbana, Syarif Nur Syifani, Sarah Taruk, Medi Wahyudianto, Mochamad Rizky Wahyudin Wahyudin Waksito, Alan Zulfikar Wardhana, Reza Wati, Masna Widians, Joan Angelina Zainal Arifin Zainal Arifin Zulfariansyah, Muhammad