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All Journal Agridevina : Berkala Ilmiah Agribisnis EDUCARE Empowerment : Jurnal Pengabdian Masyarakat JURNAL ILMIAH MAHASISWA AGROINFO GALUH JURNAL AGRIBISAINS Journal of Civic Education PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat IJIIS: International Journal of Informatics and Information Systems Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia International Journal for Applied Information Management Indonesian Journal of Business Analytics (IJBA) Jurnal Praktik Baik Pembelajaran Sekolah dan Pesantren
Eko Priyanto
Universitas Lampung
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Enhancing Housing Price Prediction Accuracy Using Decision Tree Regression with Multivariate Real Estate Attributes Utomo, Ahmar Dwi; Hayadi, B Herawan; Priyanto, Eko
International Journal of Informatics and Information Systems Vol 7, No 4: December 2024
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v7i4.226

Abstract

The real estate sector functions as a critical barometer of a nation’s economic performance; however, its inherent volatility and intricate pricing mechanisms often hinder precise valuation—particularly in developing urban markets. In the context of Indonesia, where the property industry contributes substantially to national GDP, deriving fair and data-driven housing price estimates remains a persistent challenge. Traditional appraisal methods, which rely predominantly on subjective human judgment, frequently fall short in reflecting market dynamics accurately. This research seeks to construct an interpretable machine learning framework for predicting residential housing prices by employing a Decision Tree Regression (DTR) model. The DTR method was chosen for its transparent and hierarchical structure, allowing for a clear understanding of how individual property characteristics affect price outcomes. The study utilizes a public dataset from Kaggle containing key housing attributes, including land area, building size, number of rooms, and location variables. The methodological steps encompass data preprocessing (cleaning and encoding using One-Hot Encoding), data partitioning into training and testing sets with an 80:20 ratio, and model performance evaluation using standard regression metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and the Coefficient of Determination (R²). The model attained an R² value of 0.385, suggesting that the selected features explain approximately 38.5% of the variance in housing prices. While this indicates moderate predictive capability, the DTR model offers valuable interpretive insights—particularly in identifying land area as the most influential predictor of price. The findings highlight that interpretable machine learning approaches can serve as effective analytical tools for property valuation in emerging markets, balancing predictive accuracy with transparency. Moreover, this study lays the groundwork for the future development of ensemble and hybrid predictive models, as well as the integration of AI-based analytics into decision-support systems for property valuation, investment forecasting, and urban development planning in Indonesia’s evolving real estate landscape.
Enhancing Clustering Performance through Benchmarking of Dimensionality Reduction Techniques on Educational Data Priyanto, Eko; Berlilana, Berlilana; Tahyudin, Imam
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4297

Abstract

This study evaluates the effectiveness of dimensionality reduction techniques in enhancing clustering performance using a tracer study dataset of 500 alumni from UMNU Kebumen, containing 58 variables. The objective was to identify the optimal combination of dimensionality reduction and clustering methods for uncovering patterns in alumni profiles, job search strategies, and employment outcomes. Principal Component Analysis (PCA), Non- Negative Matrix Factorization (NMF), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) were applied, followed by clustering using K-Means, DBSCAN, and Hierarchical Clustering. The findings revealed that NMF achieved the highest clustering quality, particularly with K- Means and Hierarchical Clustering, outperforming PCA. NMF also demonstrated superior compactness with a Calinski-Harabasz Index of 287.96, compared to 125.88 for PCA. While t-SNE and UMAP delivered competitive results, their computational times of 245.8 and 76.5 seconds, respectively, made them less practical for large datasets. The novelty of this study lies in its comprehensive evaluation of dimensionality reduction techniques and the integration of diverse clustering algorithms to assess their interplay. The results provide actionable insights, recommending NMF for accuracy-critical tasks and PCA for time-sensitive applications. Given the increasing volume of high-dimensional educational data, this study highlights the critical need for efficient clustering strategies to extract meaningful insights, ultimately supporting data-driven decision-making in education and workforce planning. Addressing these challenges is essential to optimizing institutional strategies, improving student employability, and enhancing workforce alignment with industry demands.
PEMETAAN POTENSI KOMODITAS HORTIKULTURA UNGGULAN DI KOTA BATU Sika; Mubarokah, Mubarokah; Priyanto, Eko
JURNAL AGRIBISAINS Vol. 9 No. 1 (2023): Jurnal AgribiSains
Publisher : Universitas Djuanda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30997/jagi.v9i1.6627

Abstract

Horticulture is the agricultural subsector that is mostly occupied by farmers, this can be seen from the amount of land use for horticulture which is more dominant than other agricultural subsectors, according to the Department of Agriculture and food security, the horticulture sector has a planting area of ​​6449,758 hectares with a total area of ​​6449,758 hectares. As many as 71 horticultural commodities are cultivated, Batu City is one of the areas in East Java that relies on the agricultural sector for its economic development, but Batu City does not yet have a clear picture of the specifications for leading commodities, especially the horticulture sub-sector. The analytical tool that can be used to determine base and non-base commodities is Location Quotient (LQ) analysis, while the tool used to classify the growth of each commodity can use Shift Share Analysis (SSA), then the results of the two analyzes are combined with the Batu City geographic map using the technique Overlay so as to produce a map of Batu City's leading commodities. With the focus of research on every sub-district in Batu City, this is because the sub-district is an integral part of the city.
The Determinants of Transfer Pricing in Energy Sector Companies Listed on the Indonesian Stock Exchange Kusbandiyah, Ani; Fakhruddin, Iwan; Mudjiyanti, Rina; Priyanto, Eko
Indonesian Journal of Business Analytics Vol. 4 No. 1 (2024): February 2024
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/ijba.v4i1.8135

Abstract

This study examines "Determinant Analysis of Transfer Pricing in Energy Sector Companies Listed on the Indonesia Stock Exchange in Energy Sector Companies". The formulation of the problem that will be studied in this research is: Does multinationality, tax haven and yhin capitalization have a positive effect on transfer pricing. And the research objective is to test and find empirical evidence of the effect of multinationality, tax havens, thin capitalization can affect transfer pricing. The sample in this study is the energy sector companies, the data that fits the sample criteria is 38 data. The analytical method used in processing the data uses multiple linear analysis. The results showed that multinationality and thin capitalization had a positive effect on transfer pricing, and tax heaven had no positive effect on transfer pricing. The results of this study are multinationality and thin capitalization have a positive effect on transfer pricing. This result is in accordance with Afifah & Prastiwi (2019) which states that multinational companies have easier access to external funding than domestic companies because funding can be obtained from various sources from the country where the company's affiliation is established.
Peningkatan Partisipasi Siswa dalam Pendidikan Kewarganegaraan melalui Kegiatan Ice Breaking Berbasis Storytelling Lea, Mella Angella; Wati, Ratna Kartika; Sadeli, Elly Hasan; Priyanto, Eko
Journal of Civic Education Vol 8 No 4 (2025): Journal of Civic Education
Publisher : Jurusan Ilmu Sosial Politik, Fakultas Ilmu Sosial, Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/jce.v8i4.1201

Abstract

This study aims to describe the implementation of storytelling ice-breaking in Pancasila Education and to analyze the factors that contribute to increased student participation in Grade 8 at SMP Negeri 4 Wadaslintang. The research used a qualitative approach with a descriptive method. Data were collected through observation, interviews, and documentation. The subjects of this study were the Pancasila Education teacher and Grade 8 students. Data analysis involved collecting data, reducing data, presenting data, and drawing conclusions. The results show that the application of storytelling ice breaking creates a more engaging learning atmosphere, increases students' motivation and self-confidence, and encourages them to be more active in participating in discussions. Some challenge remained, such as limited teacher creativity and a lack of support from the learning environment. The study concludes that storytelling ice-breaking can be an effective strategy to improve student participation in Pancasila Education learning, especially when implemented in a well-planned manner and adapted to student characteristics.
Co-Authors Achmad Affandi Ahmad Khoiri Ahmad, Aghniawati Ani Kusbandiyah Aprilia, Rennanti Lunnadiyah B. Herawan Hayadi Berlilana Berlilana Budi Prabowo Chatarina Umbul Wahyuni Eka Handriani Elly Hasan Sadeli Hengky Widhiandono, M.Si. S.E. Heru Irianto Hono Sejati, Hono Imam Tahyudin Iwan Fakhruddin Lea, Mella Angella Maemunah, Titi Mubarokah Mubarokah Mudjiyanti, Rina Nurlaili Dina Hafni Prasetyo, Priyo Agung Ragil Setiyabudi, Ragil Ratna Kartika Wati Risdianto Hermawan Saekhu, Ahmad Satibi, Imam Septiningtyas, Sisilia Sika Sri Harmianto Sri Tjondro Winarno, Sri Tjondro Sukaryorini, Pancadewi Sukawi, Zaenal Sutomo Sutomo Suyoto Suyoto Tukiran Tukiran Utomo, Ahmar Dwi Yogi Ageng Sri Legowo