Garuda - Garba Rujukan Digital

SUPPORT VECTOR MACHINE ALGORITHM FOR EARLY DETECTION SYSTEM FOR MENTAL EMOTIONAL DISORDERS IN ADOLESCENTS Muthya Cahyani Putriabhimata; Ida Widaningrum; Dyah Mustikasari
Jurnal Rekayasa Perangkat Lunak dan Sistem Informasi Vol. 5 No. 1 (2025)
Publisher : Department of Information System Muhammadiyah University of Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/seis.v5i1.8351

A mental-emotional disorder is a condition characterized by emotional fluctuations that, if left untreated, might progress into an abnormal state. In Indonesia, the treatment of mental problems is infrequently conducted due to a scarcity of psychiatric personnel and the high expenses associated with comprehensive mental health therapy and treatment. An early detection system for mental-emotional illnesses in teenagers was developed by implementing the Support Vector Machine (SVM) algorithm as a solution to this issue. The Support Vector Machine algorithm is a very accurate classification approach. This study utilizes data that is categorized into two distinct groups: anxiety and depression. The data is partitioned in an 80:20 ratio, with 80% allocated for training data and 20% for test data. The research findings indicate that the testing accuracy levels yielded a value of 85%. The value is derived using the RBF kernel with a gamma value of 0.1 and a C value 10. The Support Vector Machine model is implemented within the Graphical User Interface (GUI). The user experience questionnaire was assessed on the Graphical User Interface, resulting in a user experience score within the "good" category.

SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN DOMPET DIGITAL MENGGUNAKAN METODE ANALYTIC HIERARCHY PROCESS (AHP) Widaningrum, Ida; Dinda Septyana; Dyah Mustikasari
Jurnal Responsif : Riset Sains dan Informatika Vol 7 No 1 (2025): Jurnal Responsif : Riset Sains dan Informatika
Publisher : LPPM Universitas Adhirajasa Reswara Sanjaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51977/jti.v7i1.1801

Dalam era fintech yang berkembang pesat, dompet digital menjadi solusi keuangan yang penting namun membingungkan pengguna akibat banyaknya pilihan dan fitur. Penelitian ini bertujuan merancang dan mengimplementasikan Sistem Pendukung Keputusan (SPK) untuk pemilihan dompet digital menggunakan Metode Analytic Hierarchy Process (AHP). Dengan AHP, penelitian ini menganalisis kriteria seperti keamanan, kemudahan penggunaan, transaksi dengan merchant lain, dan pelayanan penyedia layanan untuk memberikan rekomendasi yang akurat. Hasil penelitian ini diharapkan membantu pengguna dalam membuat keputusan yang lebih terinformasi dan efisien dalam memilih dompet digital yang sesuai dengan kebutuhan mereka. Kesimpulannya, SPK berbasis AHP ini mampu memberikan rekomendasi yang lebih objektif dan dapat diandalkan, sehingga mempermudah pengguna dalam menentukan pilihan dompet digital yang optimal.

Using SVM and KNN for Predicting Customer Response Sentiment of M-PAJAK Application Muhammad Titan Rama Adi Wijaya; Ida Widaningrum; Angga Prasetyo; Dyah Mustikasari
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 11 No. 1 (2025): April 2025
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v11i1.4528

M-Pajak, an application initiated by the Directorate General of Taxes, signifies the modernization of taxation and serves a crucial function. This application facilitates taxpayers in meeting their tax obligations. User satisfaction with this application may be assessed via reviews on the Google Play Store. While this application fulfills client satisfaction, its sustained success is significantly contingent upon user contentment and experience. Sentiment analysis is essential for elucidating user evaluations and interactions with the program. This research analyses the sentiment of M-Pajak application reviews on Google Play using Support Vector Machine (SVM) and K-Nearest Neighbour (KNN), supported by the Term Frequency-inverse Document Frequency (TF-IDF) feature extraction method. A total of 1000 reviews between December 11, 2022 and December 2, 2023 were processed using KNN and SVM. The KNN algorithm yielded 153 positive predictions and 847 negative predictions and achieved 94% of accuracy. Meanwhile, SVM achieved an accuracy of 88.10%, with 325 positive predictions and 675 negative predictions. The results demonstrate the superiority of KNN in sentiment classification of M-Pajak reviews. This study also indicates that negative comments outnumber positive ones in this application. This serves as a signal for the Directorate General of Taxation to enhance user satisfaction with the M-Pajak application through continuous updates.

Recognition of the Lima Pandawa Shadow Puppet characters utilizing Principal Component Analysis (PCA) for feature extraction and K-Nearest Neighbor (KNN) for classification Ida WIdaningrum; Indah Puji Astuti; Dyah Mustikasari; Khoiru Nurfitri; Rifqi Rahmatika Az-Zahra; Rhesma Intan Vidyastari; Ali Selamat
Indonesian Journal of Information Systems Vol. 8 No. 1 (2025): August 2025
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v8i1.11032

The traditional type of puppet-shadow play, Wayang Kulit, is an integral component of Indonesian culture. The Pandawa Lima, protagonists in this artistic medium, have great importance not just in narrative but also in embodying moral and ethical principles. The automated identification of these characters can optimize a range of applications, such as instructional resources, digital preservation, and interactive displays. This research intends to maximize the advantages of PCA and KNN by utilizing their respective strengths: PCA's capacity to decrease data dimensionality and KNN's efficacy in classification tasks. An expected outcome of this combination is an enhancement in recognition accuracy without compromising computational efficiency. The classification matrix indicates that the model achieved a 78% accuracy rate. Class-specific accuracy, recall, and F1-scores are as follows: arjuna achieves a precision of 0.85, recall of 0.91, and F1 Score of 0.87. Macro averages for precision, recall, and F1 are 0.77, 0.76, and 0.74, respectively. Weighted averages for these metrics are 0.80, 0.78, and 0.77, respectively. The model exhibits strong performances on Arjuna, Sadewa, and Yudistira, but encounters difficulties with Bima and Nakula.

SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN DOMPET DIGITAL MENGGUNAKAN METODE ANALYTIC HIERARCHY PROCESS (AHP) Widaningrum, Ida; Dinda Septyana; Dyah Mustikasari
Jurnal RESPONSIF: Riset Sains & Informatika Vol 7 No 1 (2025): Jurnal Responsif : Riset Sains dan Informatika
Publisher : LPPM Universitas Adhirajasa Reswara Sanjaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51977/jti.v7i1.1801

Dalam era fintech yang berkembang pesat, dompet digital menjadi solusi keuangan yang penting namun membingungkan pengguna akibat banyaknya pilihan dan fitur. Penelitian ini bertujuan merancang dan mengimplementasikan Sistem Pendukung Keputusan (SPK) untuk pemilihan dompet digital menggunakan Metode Analytic Hierarchy Process (AHP). Dengan AHP, penelitian ini menganalisis kriteria seperti keamanan, kemudahan penggunaan, transaksi dengan merchant lain, dan pelayanan penyedia layanan untuk memberikan rekomendasi yang akurat. Hasil penelitian ini diharapkan membantu pengguna dalam membuat keputusan yang lebih terinformasi dan efisien dalam memilih dompet digital yang sesuai dengan kebutuhan mereka. Kesimpulannya, SPK berbasis AHP ini mampu memberikan rekomendasi yang lebih objektif dan dapat diandalkan, sehingga mempermudah pengguna dalam menentukan pilihan dompet digital yang optimal.

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