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All Journal Techno.Com: Jurnal Teknologi Informasi SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Informatika Global Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Reswara: Jurnal Pengabdian Kepada Masyarakat BERNAS: Jurnal Pengabdian Kepada Masyarakat Malcom: Indonesian Journal of Machine Learning and Computer Science Electronic Integrated Computer Algorithm Journal AQILA : Acceleration, Quantum, Information Technology and Algorithm Journal
Putri, Indah Pratiwi
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Service Quality Analysis of Business Licensing Information System (BLIS) at the Provincial Industry Department Using Decision Tree Putri, Indah Pratiwi; Marcelina, Dona; Yulianti, Evi; Anandez, Arum Adisha Putra
Acceleration, Quantum, Information Technology and Algorithm Journal Vol. 1 No. 2 (2024): VOLUME 1, NO 2: DECEMBER 2024
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/aqila.v1i2.48

Abstract

In the digital era, government services, particularly business licensing, are expected to be efficient, reliable, and user- friendly to meet public demands. The South Sumatra Provincial Industry Department has adopted a web-based Business Licensing Information System to facilitate the licensing process. However, the system’s effectiveness in delivering quality service and ensuring process efficiency remains underexplored. This study aims to evaluate the service quality of the system, focusing on factors such as reliability, responsiveness, assurance, empathy, and usability. Using decision tree analysis, the study identifies the key variables impacting user satisfaction and process efficiency. The research objectives include assessing the overall quality of the service, analysing factors influencing efficiency, and providing recommendations for system improvement. Data will be gathered from business users who have utilized the system within the past year. The study scope encompasses service quality dimensions, process efficiency indicators, and user satisfaction metrics. Decision tree analysis will be employed to analyse these variables, highlighting the most influential factors on system performance. This research is expected to provide insights for enhancing the system’s reliability and usability, offering data-driven recommendations for decision-makers at the Industry Department. By improving the system, users can experience a more streamlined and satisfying licensing process, ultimately increasing their likelihood to recommend and reuse the service. The findings will also contribute to public information systems literature, serving as a valuable reference for similar service evaluations and optimizations in other government sectors
Payroll Checker System Based on Employee Performance at PT GOTO Palembang Using FDD Marcelina, Dona; Putri, Indah Pratiwi; Yulianti, Evi
Acceleration, Quantum, Information Technology and Algorithm Journal Vol. 2 No. 1 (2025): VOLUME 2, NO 1: JUNE 2025
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/aqila.v2i1.78

Abstract

This study addresses the critical need for modernising payroll management at PT GOTO Palembang, a rapidly expanding enterprise grappling with the limitations of manual, paper-based salary processing. The research focuses on the design and implementation of a web-based Payroll Checker Information System that integrates employee performance metrics to enhance accuracy, efficiency, and transparency in remuneration processes. Employing the Feature-Driven Development (FDD) methodology, the system was developed iteratively to ensure alignment with organisational requirements and adaptability to evolving operational complexities. The system architecture encompasses modules for attendance tracking, performance evaluation, salary computation—including deductions, allowances, and meal subsidies—and comprehensive reporting functionalities. Role-based access controls were instituted to safeguard data integrity and facilitate secure access for administrators, employees, and directors. The implementation of this system has yielded significant improvements in payroll accuracy, reduced administrative workload, and enhanced employee trust through increased transparency. Furthermore, the integration of performance-based compensation aligns employee incentives with organisational objectives, fostering a culture of accountability and continuous improvement. This initiative exemplifies how strategic application of information technology can optimise human resource management practices, particularly in organisations experiencing rapid growth and structural complexity. The findings underscore the efficacy of agile development methodologies in delivering scalable and responsive business solutions, and they offer valuable insights for enterprises seeking to modernise their payroll systems in alignment with contemporary performance management paradigms.
A Prognostic System for Pharmaceutical Inventory Forecasting Using the Trend Least Squares Method at Rakha Medika Yulianti, Evi; Putri, Indah Pratiwi; Marcelina, Dona
Acceleration, Quantum, Information Technology and Algorithm Journal Vol. 2 No. 1 (2025): VOLUME 2, NO 1: JUNE 2025
Publisher : Yayasan Asmin Intelektual Berkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62123/aqila.v2i1.80

Abstract

This study proposes the development of a sophisticated predictive system for pharmaceutical inventory management at Rakha Medika, Palembang, aimed at addressing the prevalent challenges associated with inaccurate drug stock forecasting. Employing the Trend Least Squares method, the system leverages historical consumption data to generate precise predictions of future pharmaceutical needs, thereby facilitating optimal procurement strategies and mitigating the risks of both stockouts and surplus inventory. Developed with PHP and MySQL, the system offers a user-friendly web-based interface, providing role-specific access for administrators, warehouse personnel, and senior management, ensuring seamless integration within the existing operational framework. This research highlights the importance of data-driven decision-making in healthcare supply chain management, where the accuracy of stock forecasts directly correlates with the quality-of-service delivery. Through rigorous testing using real-world data, the system demonstrated a significant improvement in forecasting accuracy and operational efficiency, with tangible benefits including reduced administrative burdens and enhanced drug availability. The implementation of this predictive system not only optimizes inventory control but also contributes to the overall enhancement of healthcare services at the public health center.
Analisis Perbandingan Algoritma Machine Learning untuk Prediksi Stunting pada Anak: Comparative Analysis of Machine Learning Algorithms for Predicting Child Stunting Putri, Indah Pratiwi; Terttiaavini, Terttiaavini; Arminarahmah, Nur
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 1 (2024): MALCOM January 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i1.1078

Abstract

Penelitian ini menyoroti permasalahan serius stunting pada anak-anak, terutama dalam pendataan yang tidak konsisten dan kurangnya informasi akurat dalam evaluasi kondisi tersebut. Tujuannya adalah mengembangkan model Machine Learning (ML)  untuk memprediksi kasus stunting dengan lebih baik. Metode penelitian melibatkan tiga algoritma ML: Naive Bayes, K-Nearest Neighbors, dan Random Forest, dievaluasi berdasarkan Accuracy, Precision, dan recall. Penelitian ini memanfaatkan platform KNIME untuk membantu pengelolaan data yang lebih efisien dan akurat. Hasil evaluasi menunjukkan bahwa Random Forest memiliki akurasi tertinggi (87.75%) dan F1-score (0.922), menunjukkan keseimbangan yang baik antara Precision dan recall. Meskipun demikian, K-Nearest Neighbors menonjol dalam menemukan sebagian besar kasus stunting yang sebenarnya. Kesimpulannya, model Random Forest mungkin menjadi pilihan terbaik untuk mendiagnosis stunting pada anak-anak, karena kombinasi akurasi tinggi dan kemampuan menemukan kasus stunting yang lebih baik dari model lainnya. Penelitian ini memberikan wawasan tentang penerapan ML dalam mendukung deteksi dini stunting, memungkinkan intervensi yang lebih tepat dan cepat bagi anak-anak yang membutuhkan perhatian kesehatan yang lebih intensif.
Pelatihan pemanfaatan teknologi artificial intelligence bagi guru sekolah dasar Saluza, Imelda; Yulianti, Evi; Putri, Indah Pratiwi; Marcelina, Dona; Sartika, Dewi
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 8, No 2 (2024): June
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v8i2.23838

Abstract

Abstrak Dalam melaksanakan proses pembelajaran, guru dituntut untuk dapat terus menerus melakukan pembelajaran yang adaptif dan up to date yang didukung teknologi canggih. Teknologi canggih yang sering digunakan adalah Artificial Intellegence. Berdasarkan hasil observasi dan diskusi tim pengabdian kepada masyarakat (PKM) dan kepala sekolah SD Negeri 13 Palembang belum pernah mendapatkan pengetahuan memanfaatkan AI untuk mendukung proses pembelajaran dalam hal pencarian materi dan bahan ajar, karenanya tim PKM dan mitra memutuskan untuk melakukan kegiatan pelatihan memanfaatkan AI dalam pembelajaran. Dalam pelaksanaannya dilakukan dengan menggunakan metode Participatory Action Research (PAR) dengan empat tahapan yaitu perencanaan, tindakan, evaluasi dan refleksi. Setelah kegiatan dilaksanakan dilakukan analisis dari proses evaluasi dan refleksi kegiatan. Hasil evaluasi menunjukkan rata-rata peserta pelatihan mengalami peningkatan pengetahuan dan keterampilan sebesar 68,9%. Sedangkan hasil refleksi menjelaskan faktor pendukung dan kendala pelaksanaan. Adapun faktor pendukung yaitu adanya antusias peserta, dukungan kepala sekolah dan layanan internet yang lancar. Sedangkan faktor kendala antara lain adalah terdapat guru yang tidak membawa laptop, ada guru yang hampir pensiun dan merasa kurang membutuhkan pelatihan serta guru yang lupa akun email. Kata kunci: pembelajaran; materi; bahan ajar; metode PAR. Abstract In carrying out the learning process, teachers are required to be able to continuously carry out adaptive and up-to-date learning supported by advanced technology. The advanced technology that is often used is Artificial Intelligence. The community service team (PKM) and the principal of SD Negeri 13 Palembang conducted observations and had discussions. Based on their findings, they decided to conduct training activities that use AI in learning because they had no prior experience using it to support the learning process in terms of finding resources and teaching materials. Planning, action, evaluation, and reflection are the four stages of the Participatory Action Research (PAR) methods that were used in its execution. Following the completion of the task, an analysis of the assessment procedure and a contemplation of the task are conducted. According to the evaluation data, the typical training participant saw a 68.9% improvement in knowledge and abilities. In the meantime, the reflection's findings clarify the implementation's enabling elements and challenges. Enthusiastic participation, the principal's encouragement, and reliable internet access are the supporting aspects. In the meanwhile, teachers who forget their email accounts, are nearing retirement and believe they don't need training, and don't bring laptops are all restrictive factors. Keywords: learning; materials; teaching materials; PAR methods.
Penerapan Metode Forecasting Dalam Menentukan Prediksi Jumlah Mahasiswa Baru Dengan Menggunakan Single Exponential Smoothing Rama Samudra, M.S; Marcelina, Dona; Terttiaavini; Yulianti, Evi; Coyanda, John Roni; Putri, Indah Pratiwi
Jurnal Ilmiah Informatika Global Vol. 15 No. 2: Agustus 2024
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v15i2.3916

Abstract

Forecasting adalah suatu proses analisis untuk memprediksi nilai-nilai masa depan berdasarkan informasi data historis, atau tren yang telah ada. Forecasting melibatkan metode matematika, statistik, untuk menghasilkan perkiraan tentang apa yang mungkin terjadi di masa mendatang dengan mengumpulkan data historis jumlah mahasiswa baru yang mendaftar selama 11 tahun terakhir. Data harus mencakup periode waktu yang cukup lama untuk mengidentifikasi tren dan pola, kemudian analisis data historis untuk mengidentifikasi tren, musiman, atau fluktuasi lainnya. Analisis data dilakukan dengan menggunakan teknik analisis single exponential smooting akan memperhitungkan nilai alpha (konstanta smoothing) untuk menghasilkan perkiraan yang paling akurat. Analisis data menggunakan tiga nilai konstanta alpha 0.3, 0.6, 0.9 tersebut akan dipilih nilai alpha dengan nilai error terkecil, dan kemudian akan di aplikasikan pada sistem peramalan berbasis website. Melalui sistem berbasis website, informasi dapat dikelola secara efisien dan memungkinkan Universitas Indo Global Mandiri untuk mengoptimalkan proses penerimaan mahasiswa baru mereka. Dengan menerapkan metode peramalan ini dalam sistem berbasis website, Universitas Indo Global Mandiri dapat mengelola informasi secara efisien, yang pada gilirannya akan membantu mereka mengoptimalkan proses penerimaan mahasiswa baru. Analisis data dengan tingkat error terkecil, dapat diartikan bahwa metode peramalan tersebut telah memberikan hasil yang sangat mendekati dengan data aktual, sehingga dapat diandalkan untuk melakukan proyeksi ke depan.
Co-Authors Afriyani, Fauziah Amelia, Nyimas Melati Puti Anandez, Arum Adisha Putra Andriani, Annisa Arminarahmah, Nur Cahyani, Septa Coyanda, John Roni DEWI SARTIKA Dopri Evi Yulianti Heryati, Agustina Imelda Saluza, Imelda Indah Permatasari Kesuma, Hendra Di Lesfandra, Lesfandra Marcelina, Dona Mulyati Mulyati Purnamasari, Evi Rama Samudra, M.S Saputra, Tedy Setiawan Suryati Suryati Sutrisna, Rangga Terttiaavini Terttiaavini, Terttiaavini Verano, Dwi Asa