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All Journal Jurnal Terapan Abdimas Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) IJID (International Journal on Informatics for Development) Tematik : Jurnal Teknologi Informasi Komunikasi Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Journal of Computer Networks, Architecture and High Performance Computing Journal of Soft Computing Exploration Djtechno: Jurnal Teknologi Informasi KLIK: Kajian Ilmiah Informatika dan Komputer Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Manajemen Sistem Informasi Journal of Information System, Technology and Engineering Journal of Computer Science Advancements
Jayadi, Puguh
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Other

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Particle Swarm Optimization-based Linear Regression for Software Effort Estimation Jayadi, Puguh; Ahmad, Khairul Adilah binti; Cahyo, Rayhan Zulfitri Dwi; Aldida, Jofanza Denis
Journal of Information System, Technology and Engineering Vol. 2 No. 2 (2024): JISTE
Publisher : Yayasan Gema Bina Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61487/jiste.v2i2.69

Abstract

In the context of software effort estimation, this study investigates the use of Particle Swarm Optimization (PSO)-based Linear Regression to improve estimation accuracy. The main problem faced is the limitations of standard Linear Regression models in accurately estimating the effort required for software development projects. This research aims to improve the quality of estimation of software efforts to optimize resource management and project schedules. The method used was the integration of PSOs in Linear Regression, which was evaluated using three different COCOMO datasets. Experimental results show that LR+PSO models consistently outperform standard Linear Regression with lower MAE, MSE, and RMSE, as well as higher R-squared. In conclusion, integrating PSOs in Linear Regression effectively improves the estimation accuracy of software efforts, demonstrating great potential for improving estimation quality in software project management practices.
Implementasi Algoritma Decision Tree dan Support Vector Machine (SVM) untuk Prediksi Risiko Stunting pada Keluarga: Implementation of Decision Tree and Support Vector Machine (SVM) Algorithm for Stunting Risk Prediction Putri, Amanda Iksanul; Syarif, Yulia; Jayadi, Puguh; Arrazak, Fadlan; Salisah, Febi Nur
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 3 No. 2 (2023): MALCOM October 2023
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v3i2.1228

Abstract

Kondisi kekurangan gizi kronis yang disebabkan oleh asupan makanan yang tidak mencukupi sebagai akibat dari kebiasaan makan yang tidak tepat sesuai dengan gizi yang diperlukan disebut juga dengan stunting. Stunting dapat membuat fisik anak menjadi lebih pendek, selain itu dapat menghambat pertumbuhan dan perkembangan organ lain seperti ginjal, jantung, dan otak pada anak. Meningkatnya kasus stunting pada anak memerlukan upaya pencegahan secara dini. Pada penelitian ini menggunakan 18 atribut dan 5021 record data dari 10 kelurahan Kota Dumai dimana salah satu diantaranya dijadikan sebagai kelas. Pada penelitian ini menerapkan Algoritma Decision Tree dan Support Vactor Machine (SVM) untuk mengetahui algoritma mana yang tepat memproses data tersebut. Hasil prediksi dengan menggunakan Decision Tree pada penelitian ini mendapatkan nilai akurasi sebesar 96.15%, nilai recall Tidak sebesar  92.06% serta Ya sebesar 97.34% dan nilai presisi Tidak sebesar 90.99% serta Ya sebesar 97.68%. Sedangkan dengan menggunakan Algoritma SVM mendapatkan nilai akurasi sebesar 62.48%, nilai recall Tidak sebesar 99.12% serta Ya sebesar 51.80% dan nilai presisi Tidak sebesar 37.49% serta Ya sebesar 99.51%. Berdasarkan penelitian menggunakan data  tersebut dapat disimpulkan bahwa akurasai algoritma Decision Tree jauh lebih baik dibandingkan dengan algoritma SVM.
Forecasting Waste Generation with Increment Linear Regression Technique: A Case Study of SIMASKOT Application Jayadi, Puguh; Hidayati, Nasrul Rofiah; Saifulloh, Saifulloh; Hamid, Suhardi; Shuib, Salehuddin; Ismail, Siti Nurbaya
Journal of Computer Science Advancements Vol. 2 No. 5 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i5.1369

Abstract

This research aims to develop a prediction system for urban waste generation using the Incremental Linear Regression method on SIMASKOT. This method is applied to deal with the limitations of historical data, where the prediction results from the previous year are used as training data to predict the next year. The problem faced is the lack of sufficient data to create accurate and reliable prediction models in the long term. The purpose of this study is to improve the accuracy of waste generation prediction using an incremental regression approach. The experimental methodology involves the use of waste generation data from several waste categories during the period 2019 to 2022, which is then used to predict data until 2026. Model evaluation was carried out using the metrics Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R². The results show that this incremental prediction model is able to provide more accurate predictions than conventional models, especially for more volatile waste categories such as wood twigs and metals. The conclusion of this study shows that the Incremental Linear Regression technique is effective to be used in waste generation prediction, and can be integrated in long-term prediction-based monitoring applications.
Information Technology as Science: A New Model for Learning Jayadi, Puguh; Wahid, Fathul
IJID (International Journal on Informatics for Development) Vol. 8 No. 1 (2019): IJID June
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2019.08103

Abstract

The integration of learning and Information Technology (IT) currently has a good impact on education. Besides making it easy, IT also provides opportunities to achieve learning goals anytime and anywhere. Many studies have discussed how to achieve successful learning through IT. IT here only serves as a support for learning, not as a science. This research develops a model or instrument used to achieve learning success by placing IT as a science. As a result, a learning model was successfully developed by combining several perspectives related to IT; students (Background, desire, and skill), teacher (teaching), and institution (infrastructure).
Co-Authors Ahmad, Khairul Adilah binti Aldida, Jofanza Denis Anardani, Sri Anardani, Sri Aria Bima, Alim Citra Arrazak, Fadlan Cahyo, Rayhan Zulfitri Dwi Fathul Wahid Febi Nur Salisah Hamid, Suhardi Harly, Daniswara Andhika Putra Ismail, Siti Nurbaya Kelik Sussolaikah Nasrul Rofiah Hidayati, Nasrul Rofiah Pratiwi Susanti Putra, David Andrianto Putri, Amanda Iksanul R. Bekti Kiswardianta, R. Bekti Renny Sari Dewi saifulloh Saifulloh, saifulloh Shuib, Salehuddin Sussolaikah, Kelik Syaad Patmanthara Syamsiar, Syamsiar Syarif, Yulia WAHYUNI Weka Sidha Bhagawan Yudha, Yoga Prisma