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All Journal KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Sistemasi: Jurnal Sistem Informasi Jurnal Khatulistiwa Informatika Paradigma Jurnal Pilar Nusa Mandiri JUTIM (Jurnal Teknik Informatika Musirawas) Jurnal Riset Informatika JUSIM (Jurnal Sistem Informasi Musirawas) JIKA (Jurnal Informatika) BERNAS: Jurnal Pengabdian Kepada Masyarakat Bulletin of Computer Science and Electrical Engineering (BCSEE) Infotek : Jurnal Informatika dan Teknologi Journal of Computer Networks, Architecture and High Performance Computing Jurnal Sistem Informasi Jurnal Media Informatika JUSTIAN - Jurnal Sistem Informasi Akuntansi SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Jurnal Penelitian dan Pengabdian Masyarakat Jotika Journal of Accounting Information System Indonesian Community Service Journal of Computer Science (IndoComs) Jurnal Infortech
Lisnawanty
Universitas Bina Sarana Informatika
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Sistem Informasi Pengelolaan Kas Kecil pada PT. Budi Bangun Konstruksi Talia, Fani; Lisnawanty, Lisnawanty; Anna, Anna; Irmayani, Windi; Supriyatna, Adi; Lisnawanty
JAIS - Journal of Accounting Information System Vol. 2 No. 1 (2022): Juni
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jais.v2i01.1404

Abstract

PT. Budi Bangun Konstruksi merupakan perusahaan yang bergerak dalam ruang lingkup bisnis jasa kontraktor, pengadaan barang dan perdagangan. Maraknya perkembangan bisnis tersebut membuat usaha perancangan konstruksi bangunan dan interior semakin kompleks dan menjadi perhatian utama para developer. PT. Budi Bangun Konstruksi bukanlah satu-satunya perusahaan yang memberikan jasa kontraktor, pengadaan barang dan perdagangan. Dalam proses pencatatan penerimaan dan pengeluaran kas yang diterapkan di PT. Budi Bangun Konstruksi belum sesuai dengan prosedur yang ada. Maka dari itu, sistem informasi akuntansi penerimaan dan pengeluaran kas diimplementasikan pada PT. Budi Bangun Konstruksi. Pengguna dalam sistem informasi ini adalah Manager Keuangan dan Staff Keuangan pada PT. Budi Bangun Konstruksi. Berdasarkan hasil pengujian menggunakan blackbox testing diperoleh bahwa semua fungsionalitas berjalan sesuai harapan (valid).
Aplikasi Prediksi Harga Saham Telkom Indonesia Berbasis Streamlit Menggunakan Machine Learning Saputra, Juliandra; Kezia Omega Octaviani; Jimi Andrean; Fairuz Anwar Nugroho; Lisnawanty
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 2 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

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

Abstract

Fluctuations in the share price of PT Telkom Indonesia (TLKM) are a major obstacle for individual investors, who generally rely on non-numerical analysis. This study attempts to create a simple prediction solution by comparing the effectiveness of three machine learning algorithms, namely linear regression, support vector machine (SVM), and neural network (NN), then applying them to an interactive web application using the Streamlit framework. Historical TLKM stock data was prepared through a feature engineering process before performance testing. According to the comparison results, the Support Vector Machine (SVM) model proved to have the best predictive ability compared to Linear Regression and NN. The superiority of SVM can be seen from the lowest RMSE (Root Mean Squared Error) value and the highest R² (Coefficient of Determination) score, which shows that SVM is better at capturing non-linear patterns and the complexity of the TLKM market. The selected model was then integrated into the Streamlit application, which provides real-time prediction results and comparison visualizations. This research successfully bridges the gap between the accuracy of advanced machine learning models and the real needs of investors by providing an informative and easy-to-use decision-making tool.
Pelatihan Penggunaan SIAPIK Untuk Pengolahan Data Transaksi Bisnis Pada UMKM Keluarga Khatulistiwa Pontianak Nurfia Oktaviani Syamsiah; Nila Hardi; Lisnawanty; Windi Irmayani; Anna
Indonesian Community Service Journal of Computer Science Vol. 1 No. 1 (2024): Periode Januari 2024
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/indocoms.v1i1.2306

Abstract

UMKM merupakan sektor ekonomi dalam negeri yang memiliki otonomi dan potensi besar untuk meningkatkan kesejahteraan masyarakat. Dalam konteks perekonomian Indonesia, UMKM memiliki peran penting dalam menciptakan lapangan kerja, menyerap sekitar 97% dari total tenaga kerja yang ada, serta menjadi sumber investasi dengan kontribusi mencapai 60,4% dari total investasi yang terhimpun. Data ini dapat ditemukan di situs resmi Badan Koordinasi Penanaman Modal (BKPM).Tujuan pelatihan SIAPIK (Sistem Akuntansi dan Pengendalian Internal untuk UMKM) adalah meningkatkan kapasitas dan keberlanjutan UMKM melalui penerapan sistem akuntansi dan pengendalian internal yang efektif. Penelitian ini menganalisis pelaksanaan pelatihan SIAPIK dan manfaatnya bagi pelaku UMKM. Metode kualitatif digunakan dalam studi ini dengan melakukan wawancara dan observasi terhadap UMKM yang mengikuti pelatihan SIAPIK. Temuan penelitian menunjukkan bahwa pelatihan SIAPIK memiliki dampak positif pada aspek penting UMKM. Pelatihan ini membantu UMKM dalam memahami pentingnya sistem akuntansi yang baik dan pengendalian internal yang sesuai. Pelaku UMKM diberikan pengetahuan tentang laporan keuangan, pengelolaan inventaris, dan arus kas. Selain itu, pelatihan ini juga memperkenalkan penggunaan teknologi informasi yang sederhana. Dengan diadakannya pelatihan SIAPIK oleh tim pengabdian kepada masyarakat Universitas BSI,UMKM dapat meningkatkan pengelolaan usaha, mengurangi risiko kerugian, meningkatkan kepercayaan pelanggan, dan memanfaatkan teknologi informasi untuk ekspansi pasar. Pelatihan SIAPIK memiliki potensi sebagai langkah strategis dalam kemajuan sektor UMKM secara keseluruhan.
ETIKA PENGGUNAAN AI DALAM DUNIA PENDIDIKAN UNTUK GURU PAUD (HIMPAUDI) KABUPATEN KUBU RAYA Reza Maulana; Yoki Firmansyah; Lisnawanty; Ali Mustopa
Indonesian Community Service Journal of Computer Science Vol. 2 No. 2 (2025): Periode Juli 2025
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/indocoms.v2i2.9605

Abstract

This community service activity aims to improve ethical literacy in the use of Artificial Intelligence (AI) technology in Early Childhood Education (PAUD) environments, particularly in Kubu Raya Regency. Based on observations, PAUD teachers who are members of HIMPAUDI Kubu Raya still face challenges in understanding the principles of ethics, privacy, and security of children's data when using AI-based technology in learning. This program offers gradual training consisting of three levels: basic (introduction to AI and ethics), intermediate (identifying safe and appropriate AI applications), and advanced (responsible AI implementation strategies). The training is complemented by case studies, discussions of ethical issues, mentoring, and facilitation of the development of contextual guidelines based on local values. In addition, a community of practitioners is formed as a collaborative forum for teachers to share experiences and solutions. The activity outputs include increased understanding of AI ethics, the ability to select safe applications, activity documentation, scientific and popular publications, and improved quality of PAUD learning that supports holistic child development. This program is expected to encourage the creation of a culture of ethical and wise use of AI among PAUD educators.
Pengembangan Sistem Manajemen Apotek Berbasis Web dengan Fitur Prediksi Pengadaan Obat Menggunakan Random Forest Rizki Syahwal Ludiansyah; Lisnawanty Lisnawanty; Kartika Handayani
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 1 (2026): Juni 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i1.1163

Abstract

Pengelolaan persediaan stok obat merupakan salah satu aspek penting dalam operasional apotek karena berkaitan dengan ketersediaan stok dan kualitas pelayanan kepada pelanggan. Pada Apotek Rizky, pengelolaan data obat dan transaksi telah memanfaatkan sistem informasi, namun pemantauan masa kadaluarsa dan proses pengadaan obat masih belum didukung oleh fitur yang dapat membantu mengambil keputusan. Kondisi tersebut menyebabkan pengadaan obat masih dilakukan berdasarkan perkiraan sehingga berpotensi menimbulkan ketidakseimbangan stok. Penelitian ini bertujuan untuk mengembangkan sistem manajemen apotek berbasik web yang dilengkapi fitur monitoring masa kadaluarsa dan prediksi pengadaan obat menggunakan algoritma Random Forest. Metode pengembangan yang digunakan adalah System Development Life Cycle (SDLC) dengan model waterfall yang meliputi analisis kebutuhan, perancangan, implementasi, pengujian, serta pemeliharaan. Sistem dikembangkan menggunakan Laravel, Bootstrap, MySQL, Python, dan FastAPI. Hasil penelitian menunjukkan bahwa sistem mampu mendukung pengelolaan data obat, transaksi, monitoring masa kadaluarsa, melakukan prediksi dan memberikan rekomendasi pengadaan obat berdasarkan data historis penjualan. Pengujian fungsional menggunakan Black Box Testing menunjukkan seluruh fitur berjalan sesuai kebutuhan pengguna. Model Random Forest juga menghasilkan nilai Mean Absolute Error (MAE) sebesar 22,59 dan Root Mean Square Error (RMSE) sebesar 28,01. Hasil tersebut menunjukkan bahwa sistem yang dikembangkan dapat membantu operasional apotek sekaligus mendukung proses pengambilan keputusan dalam pengadaan obat.
Development of a Web-Based Smart Ecosystem Platform for Sustainable Public Service Automation Shoffan Saifullah; Muhammad Iqbal; Lisnawanty Lisnawanty; Weiskhy Steven Dharmawan; Fahmi Raditya
Jurnal Infortech Vol. 8 No. 1 (2026): June 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/infortech.v8i1.12830

Abstract

Public service delivery in Indonesia continues to face fundamental challenges including inefficient manual administrative processes, error-prone document validation, and the absence of real-time tracking systems. This research aims to develop the PANDU (Pelayanan Publik Digital Terpadu) platform as a web-based smart ecosystem that automates public services sustainably. The platform is built using the Waterfall development method with Model-View-Controller architecture based on Laravel 12 framework, Filament 4.0 administration panel, and Tailwind CSS 4.0 responsive interface. Four main smart features are integrated: automatic document validation, duplicate request detection within a 30-day window, category-based related service recommendations, and automatic priority calculation using multi-criteria scoring algorithm. The platform produces three separate panels for citizens, officers, and administrators, equipped with real-time tracking system through public API and configurable multi-step approval workflows. Black box testing results using equivalence partitioning technique demonstrate one hundred percent functional success rate, while usability evaluation using System Usability Scale yields an average score in the Excellent category with Acceptable acceptability level. The PANDU platform successfully bridges the gap between smart government theoretical frameworks and operational implementation, providing significant contribution to accelerating sustainable digital transformation of public services in Indonesia.
Perbandingan Kinerja CatBoost LightGBM dan XGBoost dengan Teknik SMOTE dan Bayesian Optimization untuk Prediksi Customer Churm pada Platform E-commerce Naina Yuniza; Lisnawanty; Kartika Handayani
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 2 (2026): September 2026
Publisher : LKP KARYA PRIMA KURSUS

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

Abstract

Churn pelanggan merupakan masalah kritis bagi bisnis e-commerce yang mengakibatkan hilangnya pendapatan dan meningkatnya biaya akuisisi. Ketidakseimbangan kelas pada dataset sering kali menurunkan performa model machine learning dalam memprediksi churn secara akurat. Penelitian ini bertujuan untuk membandingkan kinerja algoritma XGBoost, LightGBM, dan CatBoost dalam memprediksi customer churn, serta mengevaluasi pengaruh teknik penanganan ketidakseimbangan data menggunakan SMOTENC dan optimasi hyperparameter dengan Bayesian Optimization. Empat skenario eksperimen dirancang untuk mengisolasi pengaruh kedua teknik tersebut. Model dievaluasi menggunakan metrik Accuracy, Precision, Recall, F1-Score, dan AUC-ROC pada dataset E-commerce Customer Churn. Hasil eksperimen menunjukkan bahwa LightGBM secara konsisten mengungguli algoritma lainnya. Implementasi kombinasi SMOTENC dan Bayesian Optimization pada LightGBM menghasilkan performa terbaik dengan nilai Accuracy 0.9633, F1-Score 0.9635, dan AUC-ROC 0.9926. Selain itu, analisis feature importance mengungkapkan bahwa Tenure, Complain, dan CashbackAmount merupakan prediktor paling dominan. Penelitian ini berhasil menyediakan model prediktif yang sangat akurat serta memberikan wawasan strategis bagi manajemen e-commerce untuk merancang intervensi retensi pelanggan yang lebih tepat sasaran dan efisien.
Aplikasi Prediksi Harga Saham Telkom Indonesia Berbasis Streamlit Menggunakan Machine Learning Juliandra Saputra; Kezia Omega Octaviani; Jimi Andrean; Lisnawanty; Fairuz Anwar Nugroho
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 5 No 2 (2025): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol5No2.pp327-333

Abstract

Fluctuations in the share price of PT Telkom Indonesia (TLKM) are a major obstacle for individual investors, who generally rely on non-numerical analysis. This study aims to develop an easy prediction tool by testing three machine learning methods—linear regression, support vector machine (SVM), and neural network (NN)—and then using them in a user-friendly web application built with the Streamlit framework. Historical TLKM stock data was prepared through a feature engineering process before performance testing. According to the comparison results, the Support Vector Machine (SVM) model proved to have the best predictive ability compared to Linear Regression and NN. The SVM model is better because it has the lowest RMSE (Root Mean Squared Error) and the highest R² (Coefficient of Determination) score, meaning it can better understand the complex and non-linear trends in the TLKM market. The selected model was then integrated into the Streamlit application, which provides real-time prediction results and comparison visualizations. This research successfully bridges the gap between the accuracy of advanced machine learning models and the real needs of investors by providing an informative and easy-to-use decision-making tool.
ACCOUNTING INFORMATION SYSTEM FOR PURCHASING, SALES, AND SERVICE Ita Natalia; Lisnawanty Lisnawanty; Ardiyansyah Ardiyansyah
Jurnal Riset Informatika Vol. 3 No. 1 (2020): December 2020 Edition
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v3i1.50

Abstract

GM Motor Pontianak is a company engaged in the sale and service of motorbikes. Service transaction data processing and motorbike sales are still managed in a simple way, namely recording transactions into a ledger and copied to Microsoft Excel for recapitulation or report presentation. The main problem that becomes an obstacle for the company is that the admin as a transaction data processor often makes mistakes and delays in making a recapitulation of monthly sales transactions, as well as errors in making financial reports. Therefore, this study discusses the design of the accounting information system for purchases, sales, and services implemented at PT. GM Motor Pontianak. The method used in the development of this system is the waterfall model, because the system design is carried out systematically through the stages of analysis, design, implementation, testing and support. The resulting accounting information system provides facilities to two users, namely Admin and Director. Admin who can manage account data, spare part brands, spare part data, mechanical data, tariff data, account data, spare part purchase transactions, spare part sales transactions, service transactions, sales and expense transactions. The director can process user data, access spare part stock reports, purchase reports, sales reports, service service reports, mechanical incentive reports, general journal reports, general ledger reports, trial balance reports and income statements. The system built is expected to help PT. GM Motor Pontianak in managing purchases, sales and service transactions and producing financial reports in accordance with financial accounting standards.
PEMODELAN PREDIKSI TSUNAMI DENGAN MACHINE LEARNING MENGGUNAKAN PYCARET PADA DATA HISTORIS GEMPA Muhammad Iqbal; Siti Nurdiani; Lisnawanty Lisnawanty; Muhammad Fahmi Julianto
Jurnal Informatika Vol 10 No 1 (2026): JIKA (Jurnal Informatika)
Publisher : University of Muhammadiyah Tangerang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31000/jika.v10i1.15571

Abstract

AbstractIndonesia faces high tsunami risk due to its position on the Pacific Ring of Fire. This study analyzes machine learning implementation using PyCaret AutoML framework for tsunami prediction based on earthquake parameters. The dataset consists of 782 earthquake records with 13 features. Methodology includes automated preprocessing with outlier removal (16.88%), 80:20 train-test split, 10-fold cross-validation, and comprehensive evaluation. Results show XGBoost achieved best performance (93.95% accuracy, 97.19% AUC, 90.89% F1-score), LightGBM highest AUC (97.35%), Random Forest highest recall (93.11%), and SVM lowest performance (75.81% accuracy). Detailed analysis of PyCaret's automated workflow validates ensemble boosting superiority for tsunami early warning systems in Indonesia.Keywords: tsunami, machine learning, PyCaret, XGBoost, early warningAbstrakIndonesia menghadapi risiko tsunami tinggi karena posisinya di jalur Cincin Api Pasifik. Penelitian ini menganalisis implementasi machine learning menggunakan framework PyCaret AutoML untuk prediksi tsunami berdasarkan parameter gempa bumi. Dataset terdiri dari 782 rekaman gempa dengan 13 fitur. Metodologi mencakup preprocessing otomatis dengan penghapusan outlier (16,88%), pembagian data 80:20, cross-validation 10-fold, dan evaluasi komprehensif. Hasil menunjukkan XGBoost mencapai performa terbaik (akurasi 93,95%, AUC 97,19%, F1-score 90,89%), LightGBM AUC tertinggi (97,35%), Random Forest recall tertinggi (93,11%), dan SVM performa terendah (akurasi 75,81%). Analisis detail workflow otomatis PyCaret memvalidasi keunggulan ensemble boosting untuk sistem peringatan dini tsunami di Indonesia.Kata Kunci: tsunami, machine learning, PyCaret, XGBoost, peringatan dini 
Co-Authors Aan Rahman Abdul Latif Adi Supriyatna Agung Sasongko Alazmi Alazmi Alfi Azhari, Nourmala Ali Mustopa, Ali Anna Anna Anna Anna Anna, Anna Apriani Mustika Ardiyansyah Ardiyansyah Ardiyansyah Ardiyansyah Baehaqi Bambang Kurniawan Daniel Oktodeli Sihombing Daniel Oktodeli Sihombing, Daniel Oktodeli Deasy Purwaningtias Dedi Saputra Dewi Apriana Dony Marulitua Simanungkalit Fahmi Raditya Fairuz Anwar Nugroho Fairuz Anwar Nugroho Fajar Akbar Fani Talia Fara Dina, Fara Febri Wulandari firmansyah, Yoki Fuad Nur Hasan Gusti Rendani Putri Ibnu Khaldun Ibnu Khaldun Isnawati Eviana Ita Natalia Jimi Andrean Jimi Andrean Juliandra Saputra Kartika Handayani Kezia Omega Octaviani Kezia Omega Octaviani Maisyaroh Martias Martias Maulidia Maulidia Miji Muhammad Fahmi Julianto Muhammad Ikmal Tawakal Muhammad Iqbal Muhammad Iqbal Muhammad Rifqi Firdaus Muhammad Sony Maulana Naina Yuniza Nila Hardi Niswatul Ummi Norma Yunita - AMIK BSI Pontianak Novita Ganiar Anggraini Nurfia Oktaviani Syamsiah Purwaningtias, Deasy Rabiatus Saadah Reno Nurhadi Reza Maulana Reza Maulana Risdiansyah, Deni Rizka Dahlia Rizki Syahwal Ludiansyah Sa'adah, Rabiatus Saputra, Juliandra Septian, Rendi Shoffan Saifullah Siti Nurdiani Suhardjono S Talia, Fani Tarina Dashela thahir, anna Tia Rumanah Weiskhy Steven Dharmawan Windi Irmayani Yeni Mustika Yohanes Septian Hildegardis Yulia Yulia Yumi Novita Dewi