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All Journal Jurnal Teknologi Informatika dan Komputer Jurnal Informatika Dan Tekonologi Komputer
Harries Arizonia Ismail
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Other

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Analysis of Boarding House Payment Patterns Using Data Visualization Techniques to Identify Delay Factors Abid Sakti Pamungkas; Yohana Tri Widayati; Harries Arizonia Ismail
Jurnal Teknologi Informatika dan Komputer Vol. 11 No. 2 (2025): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v11i2.2841

Abstract

In an increasingly competitive business environment, the ability of Micro, Small, and Medium Enterprises (MSMEs) to survive heavily depends on effective cash flow management. Boarding house businesses, as a form of MSMEs in the service sector, face crucial challenges due to late rental payments by tenants. Management practices that are often reactive and intuitive have proven less effective in identifying the root causes of such issues. This study aims to apply an analytical approach using data visualization techniques to analyze rental payment patterns at Kost Green, Semarang. The main objective is to discover significant temporal patterns and identify tenant profile factors that strongly correlate with late payment behavior. The methodology employed is exploratory data analysis with a quantitative and visual approach, using primary data in the form of historical rental payment transactions over a one-year period, covering attributes such as tenant status and room type. The analysis process begins with a data preprocessing stage, in which a key analytical feature, Days_Late, is engineered to measure the duration of delays. The analysis is conducted using the Python programming language supported by the Pandas, Matplotlib, and Seaborn libraries. The findings reveal the existence of high-risk tenant segments (students) and critical time periods (certain months of the year) when delays tend to increase. The outcome of this research is a visual analytical report that provides a strong foundation for Kost Green management to make data-driven decisions, design more proactive and segmented billing strategies, and ultimately improve payment discipline and maintain healthy business cash flow.
Analysis of Boarding House Payment Patterns Using Data Visualization Techniques to Identify Delay Factors Abid Sakti Pamungkas; Yohana Tri Widayati; Harries Arizonia Ismail
Jurnal Teknologi Informatika dan Komputer Vol. 11 No. 2 (2025): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v11i2.2841

Abstract

In an increasingly competitive business environment, the ability of Micro, Small, and Medium Enterprises (MSMEs) to survive heavily depends on effective cash flow management. Boarding house businesses, as a form of MSMEs in the service sector, face crucial challenges due to late rental payments by tenants. Management practices that are often reactive and intuitive have proven less effective in identifying the root causes of such issues. This study aims to apply an analytical approach using data visualization techniques to analyze rental payment patterns at Kost Green, Semarang. The main objective is to discover significant temporal patterns and identify tenant profile factors that strongly correlate with late payment behavior. The methodology employed is exploratory data analysis with a quantitative and visual approach, using primary data in the form of historical rental payment transactions over a one-year period, covering attributes such as tenant status and room type. The analysis process begins with a data preprocessing stage, in which a key analytical feature, Days_Late, is engineered to measure the duration of delays. The analysis is conducted using the Python programming language supported by the Pandas, Matplotlib, and Seaborn libraries. The findings reveal the existence of high-risk tenant segments (students) and critical time periods (certain months of the year) when delays tend to increase. The outcome of this research is a visual analytical report that provides a strong foundation for Kost Green management to make data-driven decisions, design more proactive and segmented billing strategies, and ultimately improve payment discipline and maintain healthy business cash flow.
Implementasi Metode Seleksi Fitur Untuk Penentuan Indikator pada Klasifikasi Kompetensi Pemetaan Kuadran 9 di Pemprov Jateng Yudha Widi Harjanto; Harries Arizonia Ismail; Satrio Agung Prakoso
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 5 No. 3 (2025): November : Jurnal Informatika dan Tekonologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v5i3.8366

Abstract

Quadrant 9 (nine) is a model of employee competency mapping which will be used as a strategy for developing talented individuals and individuals who have weaknesses. In addition, quadrant 9 (nine) is also used in succession planning by identifying anyone who has the potential to become a leader in the organization. This study aims to obtain a data mining algorithm with a more accurate selection feature to find indicators that affect the classification of quadrant 9 competencies. The Forward Selection feature selection algorithm based on Naive Bayes is proven to be accurate and effective in determining the most influential attributes, namely IQ/Thinking Capacity, Self-Management, Social Communication, Service Orientation, Decision Making, Managing Others with 79.10% accuracy results and is included in the "Good Kappa" category.
Co-Authors Abid Sakti Pamungkas Satrio Agung Prakoso Yohana Tri Widayati Yudha Widi Harjanto