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All Journal Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Teknik Komputer AMIK BSI Jurnal Khatulistiwa Informatika JURNAL PENDIDIKAN TAMBUSAI IJISTECH (International Journal Of Information System & Technology) JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Jurnal Riset Informatika JATI (Jurnal Mahasiswa Teknik Informatika) JIKA (Jurnal Informatika) JUKI : Jurnal Komputer dan Informatika Akrab Juara : Jurnal Ilmu-ilmu Sosial IJISTECH Reputasi: Jurnal Rekayasa Perangkat Lunak Golden Ratio of Data in Summary
Luthfi Indriyani
Universitas Bina Sarana Informatika
Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Medicine & Pharmacology Social Sciences

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LAN Bandwidth Management Using the Queue Tree Method Safinatunnaza, Salwa; Noviriandini, Astrid; Indriyani, Luthfi; Fauziah, Sifa
Golden Ratio of Data in Summary Vol. 5 No. 1 (2025): November - January
Publisher : Manunggal Halim Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52970/grdis.v5i1.887

Abstract

The advancement of technology, particularly in computer networks, has enabled global connectivity through the Internet. Computer networks connecting various devices allow for information sharing and communication. One common issue is slow internet speed due to suboptimal bandwidth utilization. To address this issue, bandwidth management becomes crucial, especially in managing multiple applications at PT. XYZ, bandwidth management is implemented using a Mikrotik router using the Queue Tree method. This method allows for flexible and fair bandwidth allocation, ensuring every device has a stable internet connection. This method helps enhance efficiency and ensures bandwidth allocation is aligned with user needs, resulting in smooth and evenly distributed connectivity across the network.
APPLICATION OF THE BERT MODEL IN MEASURING USER PERCEPTION OF THE MAGIC INVESTMENT APPLICATION ON THE GOOGLE PLAY STORE Tabina Fasya Benedicta; Ade Setiawan; Luthfi Indriyani; Astrid Noviriandini; Sandra Dewi Saraswati
Akrab Juara : Jurnal Ilmu-ilmu Sosial Vol. 10 No. 4 (2025): November
Publisher : Yayasan Azam Kemajuan Rantau Anak Bengkalis

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Abstract

Investment is one of the most effective ways to achieve long-term financial gains. Nowadays, numerous digital platforms offer investment services, including the Ajaib application. The growing public interest in investing has been driven by influencers and online advertisements, yet it has also led to the rise of fraudulent schemes and fake investment platforms. Therefore, evaluating user satisfaction through sentiment analysis of application reviews becomes essential. This study aims to analyze user sentiments toward the Ajaib investment application based on reviews collected from the Google Play Store. The dataset consists of Indonesian-language reviews from the period 2019–2024, processed using Google Colab and the BERT (Bidirectional Encoder Representations from Transformers) algorithm. The classification results yielded 1,393 reviews, comprising 696 positive and 697 negative sentiments, indicating that negative opinions were slightly more dominant. The model achieved an accuracy of 85%, F1-score of 85%, recall of 85%, and precision of 87%, demonstrating that the BERT algorithm performs effectively in sentiment analysis for investment-related applications.
Decision Support System For Selection of Exemplary Employees at PT. Sinar Asia Perkasa Syahriani, Syahriani; Nurmah, Nurmah; Indriyani, Luthfi
Jurnal Riset Informatika Vol. 2 No. 4 (2020): Period September 2020
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (847.374 KB) | DOI: 10.34288/jri.v2i4.126

Abstract

Sinar Asia Perkasa is a manufacturing company, where this company is always required to innovate and improve the quality and quality of its products. Because of this, the company PT. Sinar Asia Perkasa must improve itself to get employees who have high quality and work productivity. Employees are one of the most important parts of a company that must be managed properly. To get employees of the highest quality, a process is needed that can automatically provide recommendations in selecting exemplary employees at PT. Sinar Asia Perkasa, namely by establishing a Decision Support System. This Decision Support System is expected to assist in objectively selecting employees. Making this Decision Support System using the Profile Matching method with several criteria, namely aspects of the discipline, aspects of integrity, aspects of cooperation, and aspects of work performance. Then for the final stage of this method is ranking.
Optimasi Machine Learning dalam Memprediksi Kelulusan Mahasiswa Embun Fajar Wati; Luthfi Indriyani Indriyani; Elvi Sunita Sunita; Andi Diah Kuswanto Kuswanto
Reputasi: Jurnal Rekayasa Perangkat Lunak Vol. 6 No. 2 (2025): Edisi November 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/reputasi.v6i2.11067

Abstract

Ketepatan kelulusan mahasiswa merupakan indikator penting dalam evaluasi kinerja perguruan tinggi. Keterlambatan kelulusan tidak hanya berdampak pada efektivitas proses akademik, tetapi juga pada efisiensi pengelolaan sumber daya dan reputasi institusi. Penelitian ini bertujuan untuk memprediksi ketepatan kelulusan mahasiswa menggunakan machine learning dengan jenis data mining, yaitu algoritma Decision Tree berdasarkan data kelulusan mahasiswa. Variabel yang digunakan antara lain jenis kelamin, pekerjaan, penghasilan, ipk, status pekerjaan. Metode pengujian dilakukan dengan optimasi PSO dan 10-fold cross-validation, sedangkan evaluasi model menggunakan nilai akurasi, presisi, recall, dan AUC. Hasil pengujian menunjukkan bahwa model Decision Tree memiliki kinerja yang sangat baik dalam memprediksi ketepatan kelulusan mahasiswa, dengan nilai akurasi sebesar 96.71%, presisi 92.06%, recall 99.09%, dan AUC 0.973. Temuan ini menunjukkan bahwa model efektif digunakan sebagai sistem peringatan dini untuk mengidentifikasi mahasiswa yang berpotensi terlambat lulus. Dengan demikian, pihak perguruan tinggi dapat memberikan intervensi akademik yang lebih tepat sasaran untuk meningkatkan tingkat kelulusan tepat waktu.
Co-Authors Ade Setiawan Ahmad Ikhlasul Amal Alda Meilita Andi Diah Kuswanto Kuswanto Aqshal Widianto Saputra Astrid Noviriandini Ayudilla Laili Ramadian Bachtiar, Denny Basuki Basuki Dwiza Riana Elvi Sunita Sunita Embun Fajar Wati Erna Melastuti, Erna Indradewa, Rhian Jordy Lasmana Putra Khairi, Fahmi Mai Hamni Putri Muhaimin Azis Nurmah, Nurmah Perangin-Angin, Elvi Sunita Safinatunnaza, Salwa Sandra Dewi Saraswati Septyani, Herlina Syahriani syahriani syahriani Syahriani Syahriani Syahriani Syahriani Syahriani Syahriani, Syahriani Tabina Fasya Benedicta Wahyuni, Asri weko susanto Weko Susanto Yeni Angraini