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All Journal Jurnal Informatika Universitas Pamulang Journal of Artificial Intelligence and Innovative Applications (JOAIIA) Jurnal Sistem Informasi dan Sistem Komputer JAMAIKA: Jurnal Abdi Masyarakat Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Abdi Jurnal Publikasi Inspiration: Jurnal Teknologi Informasi dan Komunikasi LOGIC : Jurnal Ilmu Komputer dan Pendidikan Biner : Jurnal Ilmu Komputer, Teknik dan Multimedia OKTAL : Jurnal Ilmu Komputer dan Sains Journal of Innovative and Creativity Riau Jurnal Teknik Informatika Indonesian Journal of Infomatics
Kecitaan Harefa
Universitas Pamulang
Humanities Astronomy Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Social Sciences Transportation Other

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Application of Multiple Linear Regression Algorithm for House Price Estimation Based on Building Location and Area to Improve Predictive Accuracy in Real Estate Valuation Kecitaan Harefa
Riau Jurnal Teknik Informatika Vol. 4 No. 3 (2025): November 2025
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v4i3.3993

Abstract

Significant differences in home prices, even on properties with similar building sizes and locations, pose a major challenge in accurately determining property valuations. The discrepancy between the actual market price and the estimated value makes it difficult for potential buyers, sellers, and developers to make the right decision. To overcome these problems, this study applied the Multiple Linear Regression (MLR) algorithm in the Decision Support System (DSS) to estimate house prices based on the location and area of the building. The dataset used consists of 545 housing data points with variables such as house prices, locations, and building areas. The research stages include data collection, pre-processing (data cleaning and normalization), model development using MLR, and model performance evaluation. The evaluation was carried out using the division of trained data and test data with an 80:20 ratio, so that the model was tested using data that was not previously trained. The results showed that the model produced a Mean Absolute Error (MAE) of 1,474,748.13, a Root Mean Squared Error (RMSE) of 1,917,103.70, and a coefficient of determination (R²) of 0.273. A relatively low R² value indicates that the location and area variables of the building are not sufficient to explain the overall variation in house prices, so the addition of other variables—such as the number of rooms, facilities, and environmental conditions—is needed to improve the accuracy of the prediction and produce a more representative price estimate.
Implementasi Metode Certainty Factor pada Sistem Pakar Berbasis Web untuk Diagnosis Gangguan Kesehatan Reproduksi Pria Endwin Levis Putra Zendrato; Kecitaan Harefa
OKTAL : Jurnal Ilmu Komputer dan Sains Vol 5 No 08 (2026): OKTAL : Jurnal Ilmu Komputer Dan Sains
Publisher : CV. Multi Kreasi Media

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Abstract

Male reproductive health disorders are among the health problems that can significantly affect quality of life, sexual function, and fertility. Limited public knowledge, reluctance to seek medical consultation due to embarrassment, and restricted access to healthcare services often lead to delayed diagnosis and treatment. This study aims to develop a web-based expert system using the Certainty Factor method to support the early diagnosis of male reproductive health disorders at Pandawa Main Clinic. The research was conducted through literature review, observation, and interviews with an andrology specialist to obtain data on diseases, symptoms, diagnostic rules, and confidence values. The system was developed using the PHP programming language with the Laravel framework and MySQL as the database management system. The Certainty Factor method was applied to calculate the confidence level of a diagnosis based on the symptoms selected by users. The system provides information on the identified disease, the confidence percentage of the diagnosis, and recommendations for initial treatment. Functional testing using the Black Box Testing method demonstrated that all system features operated according to the specified requirements. The results indicate that the developed expert system is capable of assisting users in obtaining early diagnostic information quickly, conveniently, and through online access, while also supporting healthcare professionals during the initial consultation process. This system is intended to serve as a decision-support tool and does not replace the final diagnosis or treatment provided by an andrology specialist.
Penerapan Algoritma XGBoost untuk Prediksi Tingkat Pemahaman Siswa pada Praktikum Laboratorium Komputer Kecitaan Harefa; Rinna Rachmatika; Oktafpianus Gea; Arojasa Harefa
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 2 (2026): September 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i2.1582

Abstract

Evaluasi tingkat pemahaman siswa pada praktikum laboratorium komputer masih sering dilakukan secara manual berdasarkan nilai dan observasi guru, sehingga membutuhkan waktu dan berpotensi menimbulkan subjektivitas. Penelitian ini bertujuan menerapkan algoritma Extreme Gradient Boosting (XGBoost) sebagai solusi untuk memprediksi tingkat pemahaman siswa secara objektif, cepat, dan berbasis data. Penelitian menggunakan pendekatan kuantitatif dengan 120 data siswa yang terbagi seimbang ke dalam kategori rendah, sedang, dan tinggi. Variabel prediktor meliputi nilai kuis, nilai praktikum, kehadiran, waktu pengerjaan, keaktifan, dan observasi guru. Tahapan penelitian mencakup pengumpulan dan prapemrosesan data, pengodean label, serta pembagian data secara terstratifikasi dengan komposisi 80% data latih dan 20% data uji. Model kemudian dilatih menggunakan XGBoost dan dievaluasi melalui matriks konfusi, akurasi, presisi, recall, dan F1-score. Hasil pengujian menunjukkan bahwa model berhasil mengklasifikasikan 23 dari 24 data uji dengan benar, dengan akurasi sebesar 95,83%, presisi 95,62%, recall 95,83%, dan F1-score 95,69%. Analisis kepentingan fitur menunjukkan bahwa nilai kuis merupakan variabel paling berpengaruh. Dengan demikian, XGBoost efektif digunakan sebagai pendukung evaluasi pembelajaran untuk membantu guru mengidentifikasi tingkat pemahaman siswa secara lebih akurat dan efisien.
PELATIHAN PEMANFAATAN TEKNOLOGI EVALUASI PEMBELAJARAN UNTUK MENINGKATKAN KETERAMPILAN PRAKTIKUM KOMPUTER Rinna Rachmatika; Kecitaan Harefa; Joko Priambodo; Michelle Aniela Wijaya; Ivana Aulia Sadiyah; Alvia Cinta Aprillia Wijaya
Abdi Jurnal Publikasi Vol. 5 No. 1 (2026): September
Publisher : Abdi Jurnal Publikasi

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Abstract

This community service activity addressed the limited use of digital tools for learning evaluation at SDN Pamulang Timur 01, where practicum assessment was still done manually and had not yet drawn on a Learning Management System (LMS) or an analytics dashboard. The program, originally aimed at students, was redirected to teachers after a needs assessment indicated a more urgent need at that level, following a multiplier-effect rationale. The activity aimed to strengthen teachers' capacity to design and apply technology-based evaluation instruments for computer practicum. Using a participatory training approach combining concept exposure, demonstration, hands-on practice, and personal mentoring across five stages, needs identification, module preparation, a concept workshop, hands-on practice, and post-training consultation, the two-day program (11-12 May 2026) involved 21 teachers with full school support. Day one covered the shift to objective, real-time, data-driven evaluation, plus three tools, Google Forms, Quizizz, and Kahoot!, and the use of artificial intelligence (AI). Day two focused on building instruments in Google Forms, since limited time did not allow equal practice across all three tools. Technical constraints such as unstable internet and inactive Google accounts were addressed through personal mentoring. Results are qualitative, based on observation and documentation. Every teacher completed at least one instrument, and the team left a reusable template with the school for continued, independent use of technology-based evaluation.
Co-Authors Abdullah Abdullah Aenun Nisa Alvia Cinta Aprillia Wijaya Andrian Andrian Ardianto Moenir Arif Cahyo Utomo Arojasa Harefa Cicih Sri Asih Daevi Dandi Ramdani Endwin Levis Putra Zendrato Fikri Miftahul Falah Indrajit Sumarsono Indriyani Octavia Ivana Aulia Sadiyah Joko Priambodo Kiki Nurdiansyah M. Chandra Nugraha Michelle Aniela Wijaya Muhammad Rizki Yusnadi Muhammad Zirlda Prairi Ny.Ayni Suwarni Herry Oktafpianus Gea Perani Rosyani Rinna Rachmatika Zurnan Alfian