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All Journal JUITA : Jurnal Informatika Jurnal Puan Indonesia
Nia Ekawati
Universitas Ahmad Dahlan
Computer Science & IT Economics, Econometrics & Finance Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health

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PENGENALAN APLIKASI JIRA DALAM MANAJEMEN PROYEK DI SMK YPKK 1 SLEMAN, YOGYAKARTA Rudy Ansari; Rudy Ansari; Irwansyah Irwansyah; Irwansyah Irwansyah; Nia Ekawati; Nia Ekawati; Herman Herman; Sunardi Sunardi
PUAN INDONESIA Vol. 8 No. 1 (2026): Jurnal PUAN Indonesia Vol. 8 No. 1 Juli 2026
Publisher : ASOSIASI IDEBAHASA KEPRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37296/jpi.v8i1.517

Abstract

The community service activity at SMK YPKK 1 Gamping aims to align the vocational education curriculum with industry standards through the introduction of Agile and Scrum methods using the JIRA application. The main challenge faced was the students' lack of understanding of professional project management tools, so this training focused on mastering JIRA features such as Scrum/Kanban boards and issue tracking. Through training and mentoring of 30 students, there was a significant increase in competence. Data shows that the highest score on the pre-test was 80 (9 participants), which then increased on the post-test to a perfect score of 100 achieved by 15 participants. The evaluation results concluded that 50% of participants had achieved the maximum level of understanding in operating JIRA. Thus, this implementation successfully bridged the gap between academic theory and the practical needs of the workplace, while equipping students with globally relevant project management skills.
An Indonesian Mental Health Chatbot Model Based on A Sequence to Sequence LSTM Nia Ekawati; Nia Ekawati; Imam Riadi; Herman Yuliansyah
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.30040

Abstract

This research proposes an Indonesian-language mental health chatbot model based on the LSTM Sequence-to-Sequence (Seq2Seq) architecture as an adaptive initial support solution. Unlike static classification models, this generative approach aims to capture emotional dependencies and conversational context through context vectors. The research methodology utilizes the public PSYCHIKA dataset, which includes 5,667 conversation pairs a significant volume for a low-resource language. Evaluation was conducted by comparing 80:20 and 70:30 data split schemes. Experimental results showed the best performance with the 80:20 split, achieving a BLEU-1 score of 0.137, compared to the 70:30 split, which only reached 0.043. The model achieved stable convergence at 15–16 epochs via an early-stopping mechanism without any signs of overfitting. Although training stability was maintained, the low BLEU score confirms that the use of a pure Seq2Seq LSTM without an attention mechanism is not yet sufficient to generate highly fluent responses. These findings provide a reproducible technical baseline for the development of mental health dialogue systems in Indonesia, while also emphasizing the urgency of more advanced architectures to improve the quality of empathy in the future.
Co-Authors Herman Herman Herman Yuliansyah Imam Riadi Irwansyah Irwansyah Irwansyah Irwansyah Nia Ekawati Rudy Ansari Rudy Ansari Sunardi, Sunardi