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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Informatika Jurnal Sarjana Teknik Informatika International Journal of Advances in Intelligent Informatics Jurnal Pengabdian UntukMu NegeRI Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Pemberdayaan: Publikasi Hasil Pengabdian Kepada Masyarakat Jurnal Informatika dan Rekayasa Perangkat Lunak
Supriyanto
Universitas Ahmad Dahlan
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Perancangan Arsitektur Game Virtual Academic Nofan Fahmie Wibowo; Supriyanto Supriyanto
Jurnal Sarjana Teknik Informatika Vol 10, No 1 (2022): Februari
Publisher : Teknik Informatika, Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jstie.v10i1.21077

Abstract

Game Virtual Academic (VA) berbasis mobile dapat membantu dalam memantau perkembangan akademik mahasiswa. Pada saat ini, portal akademik mahasiswa Universitas Ahmad Dahlan hanya menyajikan data berupa teks sehingga mahasiswa jarang membuka portal dan belum ada fitur untuk mengetahui skill dalam bidang akademik. Metode penelitian yang digunakan adalah Task Centered System Design (TCSD) terdiri dari tahapan analisis, perancangan, construction, pengujian. Dari penelitian ini didapatkan hasil berupa Arsitektur Game VA dengan fungsi yang telah 100% sesuai dengan task requirements yang dibutuhkan oleh sistem. Rata-rata pengujian didapatkan nilai 8.0 sehingga Arsitektur Game VA yang dikembangkan dapat dikatakan acceptable.
Implementation of the Conversational Hybrid Design Model to Improve Usability in the FAQ Supriyanto; Ika Arfiani; Zain Ahmad Taufik
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 3 (2022): Juni 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (363.414 KB) | DOI: 10.29207/resti.v6i3.3816

Abstract

FAQ is an important part of a system because it is used to make it easier for users to solve problems faced by users. Some FAQ systems have even started using Chatbot technology to make it easier for users. Chatbots have been widely used as a medium for services in almost all fields. Starting from marketing, service systems, education, health, culture and entertainment. Various types of chatbots have sprung up, ranging from text-based like short messaging applications to voice-based ones. However, not all forms of chatbot designs have been successfully implemented in the FAQ system. Adjustments need to be made, especially considering the persona of the user. This research provides a solution by implementing a hybrid conversational design. Hybrid conversation design is accomplished by incorporating text, voice, and buttons into the chatbot interface. Conversation activities with this hybrid interface provide keywords that users may search for in the form of buttons. The hybrid design of the FAQ Chatbot is proven to be able to improve user usability compared to full text chatbots and full text FAQs. The increase in user usability is measured using UEQ, the results of which show an increase in usability from all existing aspects. However, the implementation of this hybrid design also has the consequence that the conversation management system must have structured initial information.
Integrasi Model Rekomendasi Penguji Pendadaran Berbasis Text Mining pada Sistem Pengelolaan Tugas Akhir Supriyanto Supriyanto; Ardiansyah Ardiansyah; Eka Pitriyani; Muhammad Renardi Haris; Muhammad Nur Widya Luthfiantoro
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol 3, No 1 (2021): Maret
Publisher : Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/jinrpl.v3i1.3967

Abstract

Tugas Akhir adalah mata kuliah terakhir yang ditempuh oleh mahasiswa sebelum lulus dari perguruan tinggi. Tahapan tugas akhir yang dilalui kebanyakan mahasiswa biasanya adalah pendaftaran, ujian proposal, penelitian, dan ujian pendadaran. Seluruh tahapan tersebut dikelola menggunakan alat bantu berupa sistem pengelolaan tugas akhir. Alat bantu bisa berupa aplikasi spreadsheet, pengolah kata, hingga berbasis aplikasi. Akan tetapi, kelemahan dari alat bantu yang digunakan selama ini adalah fungsionalitas yang masih terpisah-pisah atau belum terintegrasi. Sebagai contoh, sebagian ada yang hanya mengelola pendaftaran saja, atau hanya pembimbingan dan ujian saja. Hal ini menyebabkan proses pengelolaan tugas akhir menjadi kurang efektif dan efisien. Penelitian ini mengusulkan pengembangan aplikasi pengelolaan tugas akhir yang terintegrasi mulai dari pendaftaran hingga ujian pendadaran. Pengintegrasian dilakukan dengan mengembangkan model rekomendasi penguji pendadaran berbasis text mining dengan vector space model yang menghasilkan ranking beserta rekomendasi jadwal pendadaran secara otomatis. Hasil penelitian menunjukkan bahwa model rekomendasi penguji masuk klasifikasi excellent recommendation dengan nilai MAP 99%. Hasil penelitian ini selanjutnya bisa diterapkan dan dimanfaatkan oleh para pengelola tugas akhir di perguruan tinggi di Indonesia.
Synergistic preprocessing approaches for improved time series analysis Andri Pranolo; Ardi Pujiyanta; Supriyanto Supriyanto
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.2321

Abstract

This paper systematically evaluates the performance of an LSTM baseline model, along with four smoothing augmentation methods (Kalman, Laplace, Moving Average, Savitzky-Golay), under different normalization strategies (Min-Max and Z-Score) for multivariate time-series forecasting. Experiments were conducted on six publicly available datasets (electricity consumption, energy consumption, sensor data, household energy, Indian electricity, and Brazilian temperature), and model performance was comprehensively compared using three metrics: MAPE, RMSE, and R². Results indicate that Laplace smoothing achieved the best performance across five datasets, effectively reducing errors while maintaining high fit quality, demonstrating its advantage in handling highly volatile and noisy time-series data. However, in some instances, Laplace smoothing, along with MA and SG methods, may produce an “over-smoothing” effect, causing forecasts to lose sensitivity to spike fluctuations. The choice of normalization strategy is equally critical: Min-Max is more suitable for data with stable distributions, while Z-Score demonstrates greater advantages for data with large numerical ranges and significant volatility. Notably, in temperature datasets with small sample sizes and high volatility, complex smoothing methods actually degraded performance, making the baseline LSTM + Z-Score the optimal choice. However, the LSTM-Laplace model with Min-Max normalization achieves the best performance among the models. Overall, the study concludes that improving prediction performance relies not only on model architecture but also on optimizing data scale, distribution characteristics, and preprocessing strategies.
Co-Authors Andri Pranolo Ardi Pujiyanta Ardiansyah, Ardiansyah Ari Nurcahya Ary Setijadi Prihatmanto Eka Pitriyani Ika Arfiani Jefree Fahana Mahendra Prabowo Muhamad Fadhil Indirwan Muhamad Fikri Muhammad Najih Farihanto Muhammad Nur Widya Luthfiantoro Muhammad Renardi Haris Nofan Fahmie Wibowo Prahara, Adhi Saputro, Tri Susanto Setyawan Wahyu Utomo Sri Winiarti Sugeng Handoko Ulaya Ahdiani Zain Ahmad Taufik