Jurnal Transformatika
Transformatika is a peer reviewed Journal in Indonesian and English published two issues per year (January and July). The aim of Transformatika is to publish high-quality articles of the latest developments in the field of Information Technology. We accept the article with the scope of Information Systems, Web Technology, Computer Networks, Artificial Intelligence, and Multimedia.
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<![CDATA[KLASIFIKASI SAMPAH ORGANIK DAN NON ORGANIK MENGGUNAKAN TRANSFER LEARNING ]]>
<![CDATA[Huta Julu, Doly Ilham Saputra]]>;
<![CDATA[Doly Ilham Saputra Huta Julu]]>;
<![CDATA[Dewi Nurdiyah]]>
<![CDATA[ Jurnal Transformatika Vol. 23 No. 1 (2025): July 2025 ]]>
Publisher : <![CDATA[Jurusan Teknologi Informasi Universitas Semarang ]]>
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DOI: 10.26623/transformatika.v23i1.12201
<![CDATA[Pengelolaan sampah di Indonesia menghadapi tantangan serius dengan 7,2 juta ton sampah belum terkelola dengan baik dari 202 kabupaten/kota, mencemari lingkungan dan menghambat daur ulang berkelanjutan. Pemilahan sampah organik dan anorganik yang masih dilakukan secara manual rentan terhadap kesalahan manusia dan tidak efisien. Penelitian ini mengembangkan model klasifikasi sampah organik dan anorganik menggunakan metode transfer learning dengan tiga arsitektur CNN: VGG16, MobileNetV2, dan ResNet50V2. Dataset diambil dari kaggle Waste Classification Data yang telah melalui proses preprocessing. Hasil eksperimen menunjukkan bahwa MobileNetV2 unggul dengan akurasi 90,13%, presisi 96,25%, dan F1-Score 87,88%, waktu inferensi 127,76 ms. Arsitektur ini memberikan keseimbangan optimal antara performa tinggi dan efisiensi komputasi, sehingga ideal diterapkan pada perangkat pintar seperti ponsel dan sistem IoT dalam konteks manajemen sampah perkotaan. Penelitian ini menegaskan efektivitas transfer learning dalam membangun sistem klasifikasi sampah yang cerdas dan efisien untuk mendukung program pemilahan sampah di tingkat rumah tangga dan institusi. ]]>
<![CDATA[Komparasi AHP, SAW, TOPSIS, VIKOR, dan MABAC pada Sistem Pengambilan Keputusan Pemilihan Supplier Obat ]]>
<![CDATA[Purnomo Putro, Dwi]]>;
<![CDATA[Eka Suryani, Puput]]>;
<![CDATA[Amri, Saeful]]>
<![CDATA[ Jurnal Transformatika Vol. 23 No. 1 (2025): July 2025 ]]>
Publisher : <![CDATA[Jurusan Teknologi Informasi Universitas Semarang ]]>
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DOI: 10.26623/transformatika.v23i1.12220
<![CDATA[The selection of pharmaceutical suppliers is crucial for ensuring consistent drug availability and maintaining service quality in healthcare facilities. This study offers a comparative analysis of five Multi Criteria Decision Making methods (AHP, SAW, TOPSIS, VIKOR, and MABAC) applied to supplier evaluation based on four key criteria: price, delivery time, receipt accuracy, and product quality. Unlike previous studies that employed individual or dual methods, this research evaluates all five methods using the same dataset to assess consistency, sensitivity, and decision reliability. The results show strong ranking consistency across methods, with AHP and SAW producing identical outputs. TOPSIS and VIKOR offer similar outcomes based on proximity and compromise analysis, while MABAC demonstrates high discrimination power for mid-ranked suppliers. Sensitivity tests confirm ranking stability under moderate weight variations. This study provides practical recommendations for selecting appropriate decision methods in pharmaceutical procurement systems based on operational context and desired decision accuracy.]]>
<![CDATA[Usability Test of Mental Health Application MoodPath with Software Usability Measurement Inventory ]]>
<![CDATA[Utomo, Victor Gayuh]]>;
<![CDATA[Widhiastuti, Hardani]]>;
<![CDATA[Heryanti, Rini]]>;
<![CDATA[Susilo, Markus Nanang I. B.]]>
<![CDATA[ Jurnal Transformatika Vol. 23 No. 1 (2025): July 2025 ]]>
Publisher : <![CDATA[Jurusan Teknologi Informasi Universitas Semarang ]]>
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DOI: 10.26623/transformatika.v23i2.12041
<![CDATA[MoodPath is a mobile application for mental health. The application uses Patient Health Questionnaire 9 (PHQ-9) and General Anxiety Disorder 7 (GAD-7) to assess mental health of its users. The study held usability test using Software Usability Measurement Inventory (SUMI) questionnaire with 27 respondents. MoodPath application got usability value of 46.26 that is Below Average in Global SUMI scales. The value is also related with every individual scale in SUMI. The Efficient, Affect, Helpfulness and Control scales have Below Average value. Only the Learnability scale has Above Average value. The usability result is reached with 95% Confidence Interval. Based on the IT skill, respondents with better IT skill gave lower usability score compared to respondent with lesser IT skill. The research also found that familiar UI and standard questionnaire (PHQ-9 and GAD-7) gave positive usability in Learnability and Efficiency scale. The research found that MoodPath application need to consider wider range of users by giving feature that not only satisfied people with lesser IT skill but also people with better IT skill. Based on the usability test, the MoodPath application may improve the usability by providing ‘Remember Me’ and result saving features.]]>
<![CDATA[Benchmarking IndoBERT and Transformer Models for Sentiment Classification on Indonesian E-Government Service Reviews ]]>
<![CDATA[Dhendra]]>;
<![CDATA[Gayuh Utomo, Victor]]>
<![CDATA[ Jurnal Transformatika Vol. 23 No. 1 (2025): July 2025 ]]>
Publisher : <![CDATA[Jurusan Teknologi Informasi Universitas Semarang ]]>
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DOI: 10.26623/transformatika.v23i1.12095
<![CDATA[The rapid adoption of e-government services in Indonesia has increased the importance of understanding public sentiment toward digital platforms. This study presents a comparative analysis of five models—IndoBERT, mBERT, XLM-R, CNN, and BiLSTM—for sentiment classification on user reviews of NEWSAKPOLE, a public service application for vehicle tax and licensing. A custom dataset of 11,000+ reviews was scraped from the Google Play Store and labeled using a hybrid rating-based and manual validation approach. Each model was evaluated using accuracy, precision, recall, and F1-score. IndoBERT achieved the highest performance with an F1-score of 0.882, outperforming multilingual and classical deep learning models. Confusion matrix analysis showed that transformer-based models were more effective in detecting neutral and mixed sentiments, while CNN and BiLSTM struggled with misclassification. The results highlight IndoBERT's robustness in low-resource sentiment analysis and its potential to enhance public service monitoring and policy feedback mechanisms in Indonesian digital governance.]]>
<![CDATA[ALGORITMA RANDOM FOREST, DECISION TREE, DAN XGBOOST UNTUK KLASIFIKASI STUNTING PADA BALITA ]]>
<![CDATA[Dhika Malita]]>;
<![CDATA[DHIKA MALITA PUSPITA ARUM]]>;
<![CDATA[KARTIKA IMAM SANTOSO]]>;
<![CDATA[ANDRI TRIYONO]]>;
<![CDATA[EKO SUPRIYADI]]>;
<![CDATA[AGUS SUSILO NUGROHO]]>;
<![CDATA[Widodo, Edi]]>
<![CDATA[ Jurnal Transformatika Vol. 23 No. 1 (2025): July 2025 ]]>
Publisher : <![CDATA[Jurusan Teknologi Informasi Universitas Semarang ]]>
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DOI: 10.26623/transformatika.v23i1.12202
<![CDATA[At the age of toddlers, children need special attention because their brains develop around 80%. Stunting is a form of long-term nutritional deficiency that occurs during the growth and development of children, which are marked with height that is not appropriate or less compared to children their age based on the standard WHO. This condition can adversely affect the cognitive development and health of children. Identifying toddlers who are at risk of experiencing stunting at an early stage is very important to reduce the adverse effects that can affect their quality of life in the future. Traditional methods are less effective in predicting stunting because they often ignore the complex factors that affect the nutritional status of toddlers. This study aims to classify stunting toddlers using Random Forest, Decision Tree, and Extreme Gradient Boost (XGBOOST) algorithms. The results obtained showed that the accuracy of the Random Forest algorithm received the highest accuracy of 99.72 %, Extreme Gradient Boost (XGBOOST) at 99.58 %, and Decision Tree received 98 87 %accuracy.]]>
<![CDATA[Sistem Pendukung Keputusan Penerima Bantuan Sosial dengan AHP dan MOORA ]]>
<![CDATA[Setiawan Adi Nugroho]]>;
<![CDATA[Nur Wakhidah]]>
<![CDATA[ Jurnal Transformatika Vol. 23 No. 1 (2025): July 2025 ]]>
Publisher : <![CDATA[Jurusan Teknologi Informasi Universitas Semarang ]]>
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DOI: 10.26623/transformatika.v23i1.12244
<![CDATA[Poverty is a multidimensional problem that requires prompt and appropriate handling to maintain a dignified human life. In Manyaran Sub-district, Semarang City, the distribution of social assistance often faces obstacles due to limited human resources and a manual selection process for recipients. Therefore, a Decision Support System (DSS) is needed to assist the selection process in a more objective and efficient manner. This study aims to develop a DSS for determining social assistance recipients in Manyaran Sub-district by combining the Analytic Hierarchy Process (AHP) and Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) methods. AHP is utilized to determine the weight of each criterion, while MOORA is used to calculate the final score of each recipient candidate. The results show that among the ten analyzed candidates, the individual coded P09 achieved the highest final score of 0.575. The top five candidates with the highest scores were declared eligible to receive social assistance, while the others were declared ineligible. The application of the AHP and MOORA methods in this DSS effectively improves the accuracy, objectivity, and efficiency of the selection process for social assistance recipients in Manyaran Sub-district. ]]>
<![CDATA[Statistical Feature Extraction Based on Wavelet Transform for Arrhythmia Detection ]]>
<![CDATA[Muwakhid, Indra Abdam]]>;
<![CDATA[Indra Abdam Muwakhid]]>
<![CDATA[ Jurnal Transformatika Vol. 23 No. 1 (2025): July 2025 ]]>
Publisher : <![CDATA[Jurusan Teknologi Informasi Universitas Semarang ]]>
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DOI: 10.26623/transformatika.v23i1.12339
<![CDATA[Early detection of arrhythmia through electrocardiogram (ECG) signals is crucial for preventing severe cardiac conditions. This study proposes a binary classification approach using statistical features derived from wavelet-transformed ECG signals. The MIT-BIH Arrhythmia Database was used, with signals filtered using a 0.5–50 Hz Butterworth bandpass filter. Signals were segmented into 360-sample windows with 100-sample overlap and labeled based on the majority annotation within each window. Wavelet transformation using Symlet 8 at level 4 was applied, followed by the extraction of eight statistical features: mean, standard deviation, variance, skewness, kurtosis, interquartile range (IQR), root mean square (RMS), and zero crossing rate (ZCR). These features were classified using MLP, KNN, and SVM models. MLP and KNN achieved the highest accuracy of 92.46%, while SVM had lower accuracy (72.99%) but high recall (94.21%). The results demonstrate the effectiveness of wavelet-based statistical features for lightweight and accurate arrhythmia detection.]]>
<![CDATA[Analisa Profile Matching Untuk Deccision Support System Pemilihan Staf Divisi Keuangan Perguruan Tinggi ]]>
<![CDATA[Prawitasari, Dian]]>;
<![CDATA[Setiawan, Aries]]>;
<![CDATA[Ichwan Setiarso, Ichwan]]>;
<![CDATA[Widjajanto, Budi]]>;
<![CDATA[Setiawanta, Yulita]]>;
<![CDATA[Pandji Mertha Agung Durya, Ngurah]]>;
<![CDATA[Ernitawati, Yenny]]>;
<![CDATA[Kusumaningrum, Lely Kusumaningrum]]>
<![CDATA[ Jurnal Transformatika Vol. 23 No. 1 (2025): July 2025 ]]>
Publisher : <![CDATA[Jurusan Teknologi Informasi Universitas Semarang ]]>
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DOI: 10.26623/transformatika.v23i1.12347
<![CDATA[Beberapa permasalahan terkait divisi keuangan diantaranya terkait keterlambatan pembuatan laporan yang disebabkan inputan data yang tidak uptodat, kurangnya kompetensi pembuatan laporan keuanga, pelayanan pembayaran mahasiswa yang belum maksimal dikarenakan staf yang kurang menguasai permasalahan pembayaran mahasiswa, kurangnya kerjasama antar staf yang mengakibatkan sebuah proses pekerjaan menjadi terhambat dan kompetensi staf yang kurang sesuai dengan pekerjaan. Staf divisi keuangan perlu memenuhi beberapa standar kualitas yang meliputi relevansi program studi dengan sub bagian dalam divisi keuangan, reputasi karir, minat, tipe kepribadian, kecermatan dan kepercayaan. Perlu dilakukan pemilihan secara obyektif dengan metode profile matching untuk menghasilkan staf yang berkompeten. Hasil pengukuran sebesar 0,89% yang menunjukkan bahwa metode ini mampu memberikan dukungan dalam proses pemilihan staf divisi keuangan ]]>
<![CDATA[Prioritas Pemakaian Anggaran Pada Klinik Kesehatan Berbasis Metode Simple Additive Weighting ]]>
<![CDATA[Karmila, Karmila]]>;
<![CDATA[Karmila]]>;
<![CDATA[Setiawan, Aries]]>;
<![CDATA[Prawitasari, Dian]]>;
<![CDATA[Dian Andika , Arditya]]>;
<![CDATA[Sjamsul Hidajat, Moch]]>;
<![CDATA[Prasetya, Jaka]]>;
<![CDATA[Hallang Lewa, Andi]]>;
<![CDATA[Kusumaningrum, Lely]]>
<![CDATA[ Jurnal Transformatika Vol. 23 No. 1 (2025): July 2025 ]]>
Publisher : <![CDATA[Jurusan Teknologi Informasi Universitas Semarang ]]>
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DOI: 10.26623/transformatika.v23i1.12397
<![CDATA[Financial management that is less careful and less able to choose each expense will have an impact on the imbalance of income and clinic expenses, if it happens continuously will cause the clinic to gradually be unable to operate anymore. Each type of clinic expense certainly has a different level of importance, the level of importance is influenced by various variables such as the nominal expenditure, deadline date, user of the expenditure, level of importance, purpose of use. The problem that occurs is that the fulfillment of the needs of the clinic is often unable to prioritize expenses or without considering the level of importance of expenses. There needs to be an appropriate priority scale to regulate the level of expenditure that will impact the financial security of the clinic so that it can support the smooth operation of the clinic, with the aim that the clinic will be able to grow or develop for the smooth provision of health services to the community. One method that can be implemented to help prioritize the use of the clinic budget is simple additive weighing. From the results of the comparison between the ranking of the old model and the ranking using the simple additive weighing method, there are two different sequences, namely codes A7 and A14, resulting in a 90% accuracy rate.]]>
<![CDATA[Analisis Kualitas Website E-Government Dengan Menggunakan Metode E-Govqual Modifikasi ]]>
<![CDATA[Mawadah, Bella Intani]]>
<![CDATA[ Jurnal Transformatika Vol. 23 No. 1 (2025): July 2025 ]]>
Publisher : <![CDATA[Jurusan Teknologi Informasi Universitas Semarang ]]>
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DOI: 10.26623/transformatika.v23i1.6912
<![CDATA[Pelayanan Electronic Government yang diberikan oleh pemerintah selalu diharapkan dapat berfungsi dengan baik untuk memberikan kualitas pelayanan yang optimal sesuai dengan tujuan organisasi untuk memenuhi kepuasan masyarakat. Pemerintah Kabupaten Kubu Raya membuat website LAPOR (Layanan Aspirasi dan Pengaduan Online Rakyat), namun laporan masyarakat di website belum sepenuhnya ditindaklanjuti sesuai batas waktu yang ditentukan dan halaman tampilan rating tidak memberikan informasi terkait rating layanan website. Penelitian ini bertujuan untuk menganalisis kualitas website LAPOR Kabupaten Kubu Raya menggunakan metode E-Govqual yang dimodifikasi dengan variabel efisiensi (EF), kepercayaan (TRS), keandalan (RLB), dukungan warga (CS), dan keseluruhan (OVR) pada kepuasan pengguna. Analisis terhadap 100 data responden menggunakan website LAPOR di Kabupaten Kubu Raya menggunakan analisis regresi linier berganda dengan 4 variabel independen pada 1 variabel dependen melalui penyebaran kuesioner. Terdapat 22 hipotesis dalam penelitian ini dengan hasil pengujian yang menyatakan bahwa 7 hipotesis berpengaruh dan berpengaruh signifikan terhadap kepuasan pengguna dengan nilai kualitas 82,6%. Hasil penelitian yang dilakukan adalah memberikan rekomendasi kepada pengelola website LAPOR Kabupaten Kubu Raya untuk memperbaiki sistem pada setiap indikator variabel yang tidak berpengaruh signifikan terhadap kepuasan pengguna. Terdapat 22 hipotesis dalam penelitian ini dengan hasil pengujian yang menyatakan bahwa 7 hipotesis berpengaruh dan berpengaruh signifikan terhadap kepuasan pengguna dengan nilai kualitas 82,6%. Hasil penelitian yang dilakukan adalah memberikan rekomendasi kepada pengelola website LAPOR Kabupaten Kubu Raya untuk memperbaiki sistem pada setiap indikator variabel yang tidak berpengaruh signifikan terhadap kepuasan pengguna. Terdapat 22 hipotesis dalam penelitian ini dengan hasil pengujian yang menyatakan bahwa 7 hipotesis berpengaruh dan berpengaruh signifikan terhadap kepuasan pengguna dengan nilai kualitas 82,6%. Hasil penelitian yang dilakukan adalah memberikan rekomendasi kepada pengelola website LAPOR Kabupaten Kubu Raya untuk memperbaiki sistem pada setiap indikator variabel yang tidak berpengaruh signifikan terhadap kepuasan pengguna.]]>
