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All Journal International Journal of Advances in Applied Sciences Jurnal Simetris Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Computatio : Journal of Computer Science and Information Systems Jurnal Informatika CogITo Smart Journal Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Teknoinfo INTECOMS: Journal of Information Technology and Computer Science JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Journal of Information Systems and Informatics Jurnal Teknologi Dan Sistem Informasi Bisnis JATI (Jurnal Mahasiswa Teknik Informatika) INFORMASI (Jurnal Informatika dan Sistem Informasi) JIKA (Jurnal Informatika) Infotek : Jurnal Informatika dan Teknologi Infotech: Journal of Technology Information Jurnal AbdiMas Nusa Mandiri Journal of Business and Audit Information System (JBASE) Jurnal Pengabdian dan Kewirausahaan Konstelasi: Konvergensi Teknologi dan Sistem Informasi Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Sains dan Teknologi
Francka Sakti Lee
Universitas Bunda Mulia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Other

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PENINGKATAN PERFORMA MODEL MACHINE LEARNING UNTUK DETEKSI DINI POLYCYSTIC OVARY SYNDROME MELALUI KOMBINASI METODE PREPROCESSING Kamila, Ahya Radiatul; Lee, Francka Sakti; Andry, Johanes Fernandes
Infotech: Journal of Technology Information Vol 11, No 2 (2025): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i2.448

Abstract

Polycystic Ovary Syndrome (PCOS) is one of the most common hormonal disorders experienced by women of reproductive age and can lead to various health problems, including menstrual irregularities, infertility, and an increased risk of metabolic diseases. Early detection of PCOS is essential to minimize long-term impacts and improve the quality of life for patients. This study aims to identify effective data preprocessing strategies to enhance the performance of classification models for PCOS detection. The dataset used is open source, consisting of 541 participants with 45 clinical and laboratory features. The main challenges encountered include the presence of many missing values, an imbalanced target class distribution, and a large number of independent features. To address these issues, a series of preprocessing steps were applied, including missing value imputation, data balancing using the Synthetic Minority Over-sampling Technique (SMOTE), and dimensionality reduction using Principal Component Analysis (PCA). A classification model was built using the Random Forest algorithm, and its performance was compared before and after applying PCA. The evaluation results show that before PCA, the model achieved an accuracy of 87.5%, precision of 86%, recall of 86%, and an F1-score of 86%. After applying PCA, performance improved to an accuracy of 90%, precision of 89%, recall of 89%, and an F1-score of 89%. These findings indicate that the right combination of preprocessing strategies, particularly SMOTE and PCA, can significantly improve the efficiency and effectiveness of models in detecting PCOS, thereby supporting the development of more reliable medical decision support systems.
RANCANG BANGUN APLIKASI INVENTORI DENGAN METODE PULL INVENTORY PADA PERUSAHAAN PERANGKAT JARINGAN Nicholas, Martinez; Lee, Francka Sakti
Infotech: Journal of Technology Information Vol 11, No 2 (2025): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i2.564

Abstract

A network equipment company specializing in the provision of switches and servers still faces challenges in stock management because the recording of outgoing goods and inventory withdrawal transactions is still done manually using notebooks and Excel. This condition causes problems such as delays in data updates, difficulties in monitoring stock availability in real time, and the risk of discrepancies between data and conditions in the warehouse. The objective of this research is to design and implement a web-based inventory application using a pull inventory approach, where stock is recorded only upon actual demand from projects or customers. This strategy helps reduce the likelihood of overstocking or understocking. Based on testing results, the developed application can support the recording of outgoing goods, provide real-time availability information, and generate useful reports for management, thereby supporting the efficiency, accuracy, and integration of the company's business processes.
PENGEMBANGAN APLIKASI REMINDER ONLINE PAYMENT KOST BERBASIS MOBILE Geasela, Yemima Monica; Christianto, Kevin; Lee, Francka Sakti; Doa, Fidelia Novena; Putri, Angie Wiyani
Infotech: Journal of Technology Information Vol 11, No 2 (2025): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i2.571

Abstract

The advancement of mobile technology provides opportunities for digital transformation in small-scale property businesses, including boarding house (kost) management. This research focuses on developing a Mobile-Based Reminder Online Payment Application for Kost using the Mobile Application Development Life Cycle (MADLC) method. The application is designed to automate payment processes through a virtual account system and provide real-time reminder notifications for both tenants and owners.The development stages included requirement identification, system design using UML diagrams, database structuring with MySQL, and interface implementation. The testing process employed User Acceptance Testing (UAT) to validate system functionality and user satisfaction. The results show that users successfully received automatic payment notifications based on the configured due dates, and all online payment transactions were accurately recorded in the system database without errors. This study concludes that the MADLC approach effectively supports the structured development of mobile applications for digital payment and reminder management in boarding houses. The UAT results confirm that the application meets user expectations in terms of functionality, ease of use, and transaction accuracy.
Predictive Maintenance of Heavy Equipment Machines using Neural Network Based on Operational Data Ahya Radiatul Kamila; Derhass, Gerry Hudera; Andry, Johanes Fernandes; Lee, Francka Sakti; Budiyanto, Very; Anatasia, Velly
CogITo Smart Journal Vol. 11 No. 2 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i2.555.229-241

Abstract

Preventive maintenance is a routine maintenance strategy that aims to maximize equipment life cycle and prevent unplanned downtime which causes increased repair costs. When carrying out this maintenance, error in selecting machines need to be anticipated to avoid company losses. This research aims to reduce human error in machine selection for preventive maintenance using deep learning. The dataset used in this research is operational data of heavy equipment machine dataset from one of the palm oil companies in Indonesia with 9 independent features and 1 dependent feature. Dependent feature is a target feature contain two target classes representing effective and ineffective machines. The dataset in this study contains outlier, feature scales that are very different, and imbalanced data class. To handle outlier and standardise data scale, the Z-score method is used. Meanwhile, the over sampling method is used to handle imbalanced data classes. To obtain the best model performance, the number of epochs and two types of optimizers (adam&adamax) of neural network are selected. In selecting the number of epochs, experiments were carried out using 100 epochs. This research obtained the linearity relationship between the number of epochs and accuracy with the accuracy values using Adam and Adamax optimizers were 94.82% and 93.11% at the 100th epoch.
RANCANG BANGUN SISTEM INFORMASI PELACAKAN STATUS PESANAN PADA USAHA PERCETAKAN Aryani, Dini Ayu; Lee, Francka Sakti
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.7739

Abstract

Penerapan sebuah sistem informasi dapat mendukung sebuah usaha dalam menjalankan kegiatan operasionalnya. Pada sebuah usaha percetakan, terdapat tantangan yang menjadi penyebab ketidakpastian bagi pelanggan untuk dapat mengetahui status pesanan mereka. Kurangnya transparasi terhadap perkembangan dari setiap tahapan produksi menjadikan terbatasnya informasi yang diperoleh pelanggan. Penelitian ini mempunyai tujuan untuk mengembangkan sistem informasi berbasis website yang diharapkan dapat mempermudah pelanggan untuk dapat memantau perkembangan status pesanan mereka. Pihak percetakan dapat mengelola pesanan dan memungkinkan untuk dapat memperbarui status pesanan pelanggan sesuai dengan tahapan produksi. Pelanggan dapat melacak status pesanan mereka secara real time tanpa perlu menghubungi pihak percetakan untuk medapatkan informasi pesanan mereka. Metode pengembangan sistem yang digunakan adalah metode Waterfall. Pengujian yang dilakukan memanfaatkan pengujian black box yang berfokus pada fungsionalitas sebuah sistem untuk memastikan sistem dapat beroperasi dengan baik. Perancangan sistem ini menerapkan solusi dari sebuah permasalahan yang ada, dapat membantu pihak percetakan dalam mengelola pesanan dan memberikan transparasi terhadap perkembangan status pesanan para pelanggan.
RANCANG BANGUN SISTEM INFORMASI ADMIN MANAJEMEN PROYEK PADA KONSTRUKSI ALUMINIUM Heber, William; Lee, Francka Sakti
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.7754

Abstract

Kegiatan konstruksi aluminium membutuhkan ketelitian tinggi dan koordinasi antar tim yang efektif, sehingga diperlukan dukungan sistem informasi manajemen proyek yang handal. Penelitian ini berfokus pada pengembangan sistem informasi admin berbasis website untuk memperbaiki proses pendataan proyek, pengelolaan jadwal, serta pencatatan pembayaran yang lebih transparan. Data diperoleh melalui observasi dan wawancara terstruktur untuk memahami keperluan pengguna dan permasalahan pencatatan manual. Sistem dikembangkan dengan menggunakan metode pengembangan sistem waterfall, yang meliputi tahap analisis kebutuhan, perancangan sistem, implementasi, dan testing. Sistem ini menyediakan fitur pengelolaan proyek, penyusunan jadwal tahapan pekerjaan, administrasi pembayaran, dan pengaturan hak akses sesuai peran pengguna. Hasil evaluasi melalui metode System Usability Scale (SUS) menghasilkan nilai rata-rata 82,63, yang menunjukkan tingkat kegunaan sistem berada pada kategori "Baik" hingga "Sangat Baik". Dengan penerapan sistem ini, efektivitas, akurasi, dan produktivitas dalam pengelolaan proyek dapat ditingkatkan. Secara keseluruhan, sistem ini mampu mendukung kelancaran operasional proyek aluminium serta memperkuat daya saing perusahaan dalam menghadapi persaingan industri yang semakin dinamis.
Co-Authors Agustina, Agustina Ahya Radiatul Kamila Alvaro, Giovanni Ananda, Deah Ananda, Vincent Ray Anatasia, Velly Andrian, Andrian Andrian, Kelvin Andry, Johanes Fernandes Anwar, Sahrul Aprilia, Keysia Arron, Rivaltino Arvin, Bryan Aryani, Dini Ayu Azhari, Ozmar Bernadus Gunawan Sudarsono Bernanda, Devi Yurisca Brainard, Aryo Breliastiti, Ririn Budiyantara, Agus Budiyanto, Very Charolina, Yanthi Charolina, Yanthi - Christianto, Kevin Christy, Vania Cornelius, Wilson Delly Vera Deny, Deny Derhass, Gerry Hudera Dinata, David Freggy Doa, Fidelia Novena Dylen, Varel Eko Ariawan Endi Putro Felicia, Jennifer Fenardi, Okky Fernando, Lukas Fernando, William Geasela, Yemima Monica Geasela, Yemima Monica Ginting, Jusia Amanda Ginting, Mega Henia Br Heber, William Hendy Tannady Honni Honni Honni, Honni Honni, Honni Huang, Calvin Ignatius Adrian Mastan Isputrawan, M. Fauzi Johanes Fernandes Andry Kamila, Ahya Radiatul Kevin Christianto KEZIA, KEZIA Lesmana, Kenvil Limawal, Isabelle Ivana Marco Antonio Narahaba Marvelino, Matthew Matthew, Randy Meyliana, Sintia Michael Pranata Monica Clara Mulyo, Jonathan Riady Nababan, Andika Jakaria Nadia Karepowan Nicholas, Martinez Nurken, Brian Prasnavira Nurprihatin, Filscha Onggo, Kallista Angelia Owen, Bryan Paramita Rosadi Piter, Dicsi Purnomo, Yunianto Putra, Rakassiwi Ayudharma Putri, Angie Wiyani Rahman, Muhammad Ryo Reynaldi Ekklesia Rudi, Ardian Brian Pratama Samuel Winata Santoso, Andi Putra Setiawan, Selly Shiang Lung Felix Stevanus Stevanus Steven Steven Sudarsono, Bernandus Gunawan Sulaeman, Asvian Suryantara, I Gusti Ngurah Tampinongkol, Felliks F Tampinongkol, Felliks F. Teady Matius Surya Mulyana, Teady Matius Verawaty Verawaty Wandy Wandy Wiedjaya, Handry Wijaya, Agustinus Frits Wijaya, Hermawan William Darma Wincent, Wincent Witari, Putu Sita Witari, Putu Sita Yusup, Christian Ronaldo