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All Journal CCIT (Creative Communication and Innovative Technology) Journal Jurnal ICT : Information Communication & Technology
Yodi Susanto
Fakultas Teknologi Informasi, Ilmu Komputer, Universitas Budi Luhur, Jakarta Selatan
Computer Science & IT

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Designing Application For Defect Recording and Handover Of Property Based On Mobile Application by Applying SQLite Technology Persis Haryo Winasis; Raga Maulana; Yodi Susanto
CCIT (Creative Communication and Innovative Technology) Journal Vol 13 No 2 (2020): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (872.952 KB) | DOI: 10.33050/ccit.v13i2.990

Abstract

Property development companies that produce housing products, high rise dwellings, and office buildings generally have data on the quality of buildings, one of which is obtained during the defect inspection process between developers and consumers before handing over units. Recording data is generally still done manually using a form on a paper. For these conditions, researchers tried to build an application based on mobile apps to digitally record the defect checklist of the dwelling so that the data collected can be processed for the needs of analysis and development strategies. Difficulties encountered during the unit handover process using digital methods on the newly completed property, one of which is the quality of data and internet signals. Mobile apps certainly require a data signal connection to send data to the server. This Android-based mobile apps will implement SQLite technology which allows the recording of transactions to be done locally first, which can then be synchronized into the database server after getting the required internet data connection. SQLite was chosen because it has a relatively small library code unlike relational DBMS in general. SQLite is also easy to use without complex configurations. With the support of the ease of function of SQLite it also allows applications to be integrated with the property sales application system.
Penerapan Algoritma C4.5 Pada Imbalanced Dataset Untuk Memprediksi Kegagalan Angsuran Properti Yodi Susanto; Devit Setiono; Muhammad Syafrullah
Jurnal ICT : Information Communication & Technology Vol 20, No 2 (2021): JICT-IKMI, Desember 2021
Publisher : STMIK IKMI Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36054/jict-ikmi.v20i2.372

Abstract

In this research, the data collection carried out by studying the patterns of consumers who fail to pay, which aimed to build a model so that it could be used in predicting customers who have the potential to fail to pay. The research used the Cross-Industry Standard Process for Data Mining (CRISP-DM) method with details of the business understanding process, data understanding, data preparation, modeling, evaluation and deployment / interpretation. The dataset in this research was taken from sales, cancellation and consumer data from January 2016 to December 2019. Because the dataset in this research was an imbalanced dataset, the researchers tried to use Synthetic Minority Oversampling Technique (SMOTE) in handling the imbalanced dataset. The research conducted a comparison of the value of accuracy, precision, recall, f measure and Area Under the ROC Curve (AUC) between the original dataset and the dataset for the addition of the SMOTE technique to several algorithms including C4.5, K-NN and Naïve Bayes. The attributes used in this research were source of funds, purpose of purchase, age, selling price, occupation, total installments, percentage of total installments, monthly installments, percentage of late installments and status. From the comparison, it was found that the C4.5 algorithm with the SMOTE 480% dataset had the highest accuracy value of 97.62%, precision of 0.976, recall of 0.976, f measure of 0.976 and AUC of 0.986 which meant Excellent Classification. From the research conducted, it was expected that the model formed on the imbalanced dataset with the C4.5 and SMOTE algorithms could be used to predict consumer installment failures.
Penerapan Algoritma C4.5 Pada Imbalanced Dataset Untuk Memprediksi Kegagalan Angsuran Properti Devit Setiono; Yodi Susanto; Mohammad Syafrullah
Jurnal ICT: Information Communication & Technology Vol. 21 No. 2 (2021): JICT-IKMI, Desember 2021
Publisher : LPPM STMIK IKMI Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In this research, the data collection carried out by studying the patterns of consumers who fail to pay, which aimed to build a model so that it could be used in predicting customers who have the potential to fail to pay. The research used the Cross-Industry Standard Process for Data Mining (CRISP-DM) method with details of the business understanding process, data understanding, data preparation, modeling, evaluation and deployment / interpretation. The dataset in this research was taken from sales, cancellation and consumer data from January 2016 to December 2019. Because the dataset in this research was an imbalanced dataset, the researchers tried to use Synthetic Minority Oversampling Technique (SMOTE) in handling the imbalanced dataset. The research conducted a comparison of the value of accuracy, precision, recall, f measure and Area Under the ROC Curve (AUC) between the original dataset and the dataset for the addition of the SMOTE technique to several algorithms including C4.5, K-NN and Naïve Bayes. The attributes used in this research were source of funds, purpose of purchase, age, selling price, occupation, total installments, percentage of total installments, monthly installments, percentage of late installments and status. From the comparison, it was found that the C4.5 algorithm with the SMOTE 480% dataset had the highest accuracy value of 97.62%, precision of 0.976, recall of 0.976, f measure of 0.976 and AUC of 0.986 which meant Excellent Classification. From the research conducted, it was expected that the model formed on the imbalanced dataset with the C4.5 and SMOTE algorithms could be used to predict consumer installment failures.
Co-Authors Devit Setiono Mohammad Syafrullah Persis Haryo Winasis Raga Maulana