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All Journal Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika Moneter : Jurnal Akuntansi dan Keuangan Jurnal Pilar Nusa Mandiri JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Mantik Penusa JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Mantik Indonesian Journal of Business Intelligence (IJUBI) Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Teknologi Informatika dan Komputer Journal of Students‘ Research in Computer Science (JSRCS) Akasia: Artikel Ilmiah Sistem Informasi Akuntansi Jurnal Abdimas Komunikasi dan Bahasa International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET) Journal of Accounting Information System AMMA : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika dan Teknologi Informasi
Veti Apriana, Veti
Universitas Bina Sarana Informatika
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Neuroscience Nursing Social Sciences Transportation

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APPLICATION OF THE SMART METHOD FOR PROVIDING SCHOLARSHIPS IN HIGH SCHOOLS Veti Apriana; Sifa Fauziah; Wati Erawati
International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) Vol. 2 No. 12 (2023): NOVEMBER
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54443/ijset.v2i12.302

Abstract

Providing scholarships is an important step to support the nation's best sons and daughters in pursuing education up to the tertiary level. Many agencies and companies provide scholarships as a form of assistance to ensure the sustainability of the nation's next generation in the future. Nevertheless, the implementation of scholarships at the senior high school level still raises questions regarding conformity with the targets and criteria that have been set. This study aims to apply the Simple Multi Attribute Rating Technique (SMART) method in determining scholarship recipients for high school students. By using the SMART method, it is hoped that the selection process for scholarship recipients can be more effective and fair with the criteria used such as class ranking scores, parents' income, number of dependents on parents, and non-academic achievements. The end result of applying the SMART method is in the form of student rankings indicating their chances of getting a scholarship. The higher the ranking obtained, the greater the opportunity for students to receive scholarships, the highest ranking was achieved by student number 19 with an acquisition value of 97, indicating that this student is entitled to a scholarship. This study shows that the SMART method can be implemented in a decision support system to determine scholarship recipients for high school students.
Menentukan Kelayakan Pemberian Pinjaman Menggunakan Metode MOORA (Multi-Objective Optimization on The Basis Of Ratio Analysis) Apriana, Veti; Erawati, Wati; Fauziah, Sifa
Jurnal Teknologi Informatika dan Komputer Vol. 10 No. 1 (2024): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v10i1.2085

Abstract

Dunia perkreditan sangatlah dekat dengan kehidupan masyarakat yang konsumtif namun tidak hanya itu, kehadiran kredit atau pinjaman  kini juga dimanfaatkan oleh sebagian orang untuk keperluan produktif seperti membangun bisnis. Sebagian besar masyarakat menjadikan kredit sebagai salah satu solusi dalam menyelesaikan berbagai masalah keuangan. Dalam memberikan pinjaman tentunya pihak Bank harus melakukan penelitan dan perhitungan yang jeli terhadap calon nasabah. Kesalahan dalam menentukan kelayakan calon nasabah peminjam akan berimbas pada pembayaran kredit yang macet. Berdasarkan permasalahan tersebut diperlukan adanya metode untuk menentukan kelayakan pemberian pinjaman, dalam penelitian ini menggunakan metode MOORA (Multi-Objective Optimization on The Basis of Ratio Analysis). Kriteria yang digunakan yaitu status kepemilikan rumah, penghasilan utama, kemampuan angsuran perbulan, jaminan kredit, status usaha, kondisi usaha, penghasilan tambahan, dan kepribadian. Hasil penelitian menunjukan bahwa hasil keputusan dengan alternatif terbaik didapat pada C2 dengan perolehan nilai sebesar 0,4591. Hasil pengolahan data kelayakan pemberian pinjaman dengan menggunakan MOORA dapat di implementasikan dalam sebuah sistem pendukung keputusan untuk melakukan penilaian kelayakan pemberian pinjaman sehingga dapat membantu pihak yang berwenang dalam mengambil keputusan yang sesuai dengan kriteria yang ada. Dengan menggunakan metode MOORA dalam menyelesaikan permasalahan pemberian pinjaman yang mempunyai kriteria-kriteria yang menghasilkan perankingan, sehingga memudahkan pihak yang berkepentingan dalam menyimpulkan pemohon kredit yang terpilih dalam keputusan pemberian pinjaman.
Analisis Pemanfaatan Software Accurate Versi 4 Dalam Menghasilkan Laporan Keuangan Menggunakan Metode PIECES Apriana, Veti; Erzab, Anggi Okliata; Fauziah, Sifa
JAIS - Journal of Accounting Information System Vol. 4 No. 2 (2024): Desember
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/jais.v4i2.7459

Abstract

Menghadapi kondisi persaingan yang semakin ketat, perusahaan dituntut untuk menjalankan operasionalnya secara lebih efektif dan efisien. Tingginya tingkat persaingan, disertai dengan perkembangan perekonomian dan kemajuan teknologi sistem informasi, memiliki peran yang sangat penting dalam mendukung kemajuan perusahaan namun, PT. Target Makmur Sentosa masih menghadapi kendala karena belum memanfaatkan sistem informasi secara optimal. Hal ini sering menyebabkan kekeliruan dalam proses pencatatan dan penghitungan transaksi dan laporan keuangan, yang berdampak pada hasil yang kurang akurat. Selain itu, waktu yang diperlukan untuk menyusun laporan keuangan menjadi kurang efisien, sehingga menimbulkan kesulitan dalam penyajian laporan neraca secara tepat waktu. Penelitian ini bertujuan untuk menganalisis pemanfaatan penerapan software Accurate Versi 4 di PT. Target Makmur Sentosa dalam penyusunan laporan keuangan agar proses pengolahan data menjadi lebih efisien, andal, relevan serta presisi sesuai dengan standar akuntansi yang berlaku. Penelitian ini menggunakan metode analisis PIECES dan untuk data penelitian dikumpulkan melalui pendekatan metode kualitatif, dengan cara observasi, wawancara, dan studi literatur Hasil analisis penelitian menunjukkan bahwa penerapan Accurate mampu meningkatkan akurasi, efisiensi, dan relevansi laporan keuangan, serta mengurangi kesalahan manual yang sering terjadi pada metode konvensional. Selain itu, software Accurate memastikan laporan yang dihasilkan sesuai dengan standar akuntansi yang berlaku, sehingga perusahaan dapat memenuhi kewajiban pelaporan keuangan secara efisien, presisi, dan andal.
Implementation of YOLOv8 and DETR for Multi-Level Tomato Ripeness Detection with Real-Time Bounding Boxes Muhammad Rizky Heriadi Putra; Deni Setiawan; Ahnaf putra hafezi; Rachmat Adi Purnama; Veti Apriana; Rame Santoso
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 8 No. 2 (2025): Jurnal Teknologi dan Open Source, December 2025
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v8i2.5083

Abstract

Tomato ripeness detection is an essential component in the development of automated agricultural systems, enabling improvements in harvesting accuracy, sorting consistency, and supply chain standardization. Conventional grading processes rely heavily on manual observation, which is subjective, labor-intensive, and unsuitable for large-scale operations. Recent advancements in deep learning enable automated recognition of visual maturity indicators through object detection frameworks, offering a more reliable and scalable solution. This study examines the implementation of two modern detection models, YOLO and DETR, for multi-level tomato ripeness classification involving four distinct maturity stages. The research workflow includes dataset collection, annotation, preprocessing, model training, threshold calibration, and systematic evaluation to assess detection stability and classification behavior under real-world variability.Analysis of model outputs demonstrates that both architectures are capable of identifying multiple ripeness stages with useful levels of consistency, although each model exhibits strengths under different operational conditions. YOLO provides advantages in scenarios requiring real-time responsiveness and deployment on resource-limited hardware, making it suitable for mobile automation and field-based harvesting systems. DETR shows improved interpretive behavior in visually complex environments, particularly when samples exhibit subtle maturity differences or appear in overlapping cluster formations. The findings indicate that no single model is universally optimal and that deployment choice should be based on application requirements, environmental constraints, and operational objectives. This research contributes practical insight to the integration of artificial intelligence in agriculture and provides a foundation for future work exploring model fusion, advanced feature learning, or multispectral input integration to further enhance maturity classification performance.
Penerapan Metode Simple Multi Attribute Rating Technique untuk Pemilihan Supplier Veti Apriana; Sifa Fauziah
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 8, No 3 (2025): Juni 2025
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v8i3.9217

Abstract

Abstrak - Manajemen rantai pasokan (SCM) adalah serangkaian tindakan yang mencakup koordinasi, penjadwalan, dan pengendalian proses pengadaan, produksi, persediaan, dan distribusi produk atau layanan. Dalam hal ini, pemilihan supplier menjadi komponen penting yang memengaruhi efisiensi dan keberhasilan operasi bisnis. Kelancaran distribusi dan produksi dipengaruhi langsung oleh kinerja pemasok, terutama dalam hal kebijakan persediaan. Permasalahan yang kerap terjadi yaitu banyak perusahaan masih memilih supplier berdasarkan intuisi, pengalaman masa lalu, atau hanya berdasarkan harga termurah tanpa mempertimbangkan faktor lain secara objektif. Oleh karena itu, untuk menilai berbagai kriteria yang diperlukan untuk memilih supplier, diperlukan metode evaluasi yang sistematis dan objektif. Salah satunya, dapat menggunakan pendekatan Simple Multi-Attribute Rating Technique (SMART) untuk membantu perusahaan mengevaluasi dan memilih supplier secara terstruktur berdasarkan kriteria seperti harga, kualitas, pelayanan, lokasi, kebijakan persediaan supplier, fleksibilitas kontrak dan logistik. Hasil analisis menunjukkan bahwa pendekatan dengan metode SMART mampu memberikan rekomendasi pemilihan supplier yang optimal, yang membantu pengambilan keputusan strategis dalam manajemen rantai pasokan.Kata kunci: Manajemen Rantai Pasok; Pemilihan Pemasok; Metode SMART; Pengambilan Keputusan Multikriteria; Keputusan Strategis. Abstract - Supply Chain Management (SCM) encompasses a series of coordinated actions involving procurement, production, inventory management, and the distribution of goods or services. Within this framework, supplier selection plays a pivotal role in ensuring operational efficiency and business success. Supplier performance, particularly in terms of inventory policy, directly impacts the continuity of production and distribution. However, many companies still rely on intuition, past experiences, or price alone in choosing suppliers, often neglecting other essential evaluation criteria. To address this, a systematic and objective method is required to assess multiple attributes in supplier selection. The Simple Multi-Attribute Rating Technique (SMART) offers a structured approach for evaluating suppliers based on key criteria such as price, quality, service, location, inventory policy, contract flexibility, and logistics. Findings indicate that the SMART method can generate optimal supplier recommendations, thereby supporting strategic decision-making in supply chain management.Keywords: Supply Chain Management; Supplier Selection; SMART Method; Multi-Criteria Decision Making; Strategic Decision Making.
KLASIFIKASI KEMATANGAN PISANG BERDASARKAN CITRA WARNA KULIT MENGGUNAKAN DECISION TREE DAN SUPPORT VECTOR MACHINE DENGAN INTEGRASI YOLOV8 Gitisari, Deva; Nisrina, Restu Putri; Putri, Nayla Natania; Heristian, Sujiliani; Apriana, Veti; Santoso, Rame
Indonesian Journal of Business Intelligence (IJUBI) Vol 8 No 2 (2025): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v8i2.6488

Abstract

  Di Indonesia, panen pisang sering dilakukan sebelum buah mencapai kematangan fisiologis. Akibatnya, seringkali pisang yang belum matang beredar di pasaran. Tujuan dari penelitian ini adalah untuk mengevaluasi akurasi dua algoritma Machine Learning, yaitu Decision Tree dan Support Vector Machine (SVM) untuk menentukan tingkat kematangan pisang dengan  menggunakan dataset 6000 gambar pisang yang dikategorikan unripe, ripe, overripe, dan rotten. Dataset dipecah dalam rasio 80:20 untuk data latih dan data uji. Kemudian, metrik akurasi, presisi, recall, dan skor F1 digunakan untuk menguji. Hasil pengujian menunjukkan algoritma SVM memiliki akurasi tertinggi 92%, melampaui Decision Tree yang memiliki akurasi 82%. Model SVM Terbaik kemudian dikombinasikan dengan YOLOv8 untuk identifikasi kematangan pisang secara real-time menggunakan kamera. Penelitian ini memberikan kontribusi dengan menunjukkan efektivitas kombinasi HSV-SVM serta implementasi real-time menggunakan YOLOv8 menawarkan solusi praktis untuk pemantauan kualitas pisang secara otomatis.
Sistem Kecerdasan Buatan Untuk Deteksi Kondisi Daun Berbasis Metode Klasifikasi Fahrozi, Habil; Adiansyah, Rifky Ramadhan; Samit, Zaidan; Sujiliani, Sujiliani; Santoso, Rame; Apriana, Veti
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 8 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i1.2315

Abstract

Plant diseases pose a significant threat to agricultural productivity. This study aims to develop and evaluate an artificial intelligence system capable of automatically detecting leaf health conditions and comparing the performance of two different deep learning architectures. Leaf image data obtained from the Kaggle dataset were processed and classified using Convolutional Neural Network (CNN) and MobileNetV2, while the YOLOv8 algorithm was applied to detect leaf objects within the images. The main evaluation metric used was classification accuracy to assess the model’s ability to identify whether a leaf is healthy or diseased. The results demonstrate the efficiency and comparative performance of both classification methods. The best-performing model was then implemented into a Python-based web application, enabling users to upload leaf images and obtain real-time health detection results. This implementation provides a practical contribution to the development of precision agriculture systems.
Sistem Deteksi Penggunaan Helm Pada Pengendara Sepeda Motor di Indonesia Menggunakan Perbandingan Model YOLOv8 dan RT-DETR Samuel Orief Rosario; Agustinus Aditya Bintara; Muhammad Rifki Zhaki; Rachmat Adi Purnama; Rame Santoso; Veti Apriana
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6314

Abstract

Road safety is an important aspect in reducing accident risks, especially for motorcycle riders. To improve compliance with helmet use, this study compares the performance of two deep learning–based object detection models, namely YOLOv8 and RT-DETR, using a Roboflow dataset consisting of 3,735 images with two classes: with helmet and without helmet. The research process includes data acquisition, preprocessing (512×512 pixels), model training conducted in Visual Studio Code using an Nvidia GTX 1070 Ti GPU with the Ultralytics framework (100 epochs, AdamW optimizer, 0.0005 learning rate, 25 patience), testing on images, videos, and real-time inputs using last.pt, as well as evaluation through precision, recall, mAP, and confusion matrix, followed by implementation of the best algorithm in a local Streamlit web application.The results show that RT-DETR achieved slightly better training performance in terms of mAP50–95, while YOLOv8 performed better during real-world testing with more stable accuracy, particularly for the with helmet class. YOLOv8 reached up to 100% accuracy in video and real-time testing, whereas RT-DETR performed better in the without helmet class, achieving 95% accuracy on image data and up to 100% in video testing. Overall, YOLOv8 was selected as the best model for implementation in the Streamlit-based helmet detection application because it is faster, more stable, and more accurate. This system has the potential to support intelligent ETLE enforcement to enhance traffic safety in Indonesia.
Klasifikasi Wajah untuk Rekomendasi Gaya Rambut Menggunakan SVM dan Random Forest Mochamad Rizky Ainur Ridho; Mahatma Mahesa; Bagus Adi Wibowo; Rachmat Adi Purnama; Veti Apriana; Rame Santoso
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6360

Abstract

The goal this project is to create a face-shape classification and hairstyle recommendation system by combining Support Vector Machine (SVM) and Random Forest (RF) algorithms with Histogram of Oriented Gradients (HOG) feature extraction. This study is motivated by the growing demand for individualized appearance support, as many users find it difficult to find haircuts that complement their face features. The method first preprocesses facial photos, uses HOG to extract key geometric and texture-based features, and then uses SVM and RF models to categorize the images. For training, validation, and testing, a dataset of five different face shapes is utilized. According to experimental results, the Random Forest model has an accuracy of about 89%, while the SVM model achieves an accuracy of about 95%. These findings suggest that SVM is better suited for managing high-dimensional feature spaces generated by HOG extraction. A recommendation system that offers hairstyle recommendations based on the anticipated face shape is then integrated with the trained model. The system is useful for real-time use since it can process pictures taken with the camera or uploaded from the gallery. Overall, this study shows that integrating HOG with SVM offers a dependable basis for creating customized hairdo recommendations as well as an efficient method for face-shape classification.  
Sistem Deteksi Penyakit pada Tanaman Cabai Menggunakan RT-DETR dan YOLLOv8 Pedro Lucio Parera; Gregorius Bayu Listyoputro; Krisnavaro Raihananta; Rachmat Adi purnama; Rame Santoso; Veti Apriana
Jurnal Teknik Informatika dan Teknologi Informasi Vol. 5 No. 3 (2025): Desember: Jurnal Teknik Informatika dan Teknologi Informasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jutiti.v5i3.6373

Abstract

This study investigates the performance of two state-of-the-art object detection models, YOLOv8 and RT-DETR, in identifying diseases in chili plants, which represent a major challenge affecting horticultural productivity. Diseases such as anthracnose and Cercospora leaf spot often cause significant yield losses, and traditional manual identification tends to be inefficient, subjective, and error-prone due to the visual similarities found among disease symptoms. The objective of this research is to evaluate and compare the capabilities of both models using the Chili dataset from Roboflow Universe consisting of four classes: Anthracnose, Cercospora Leaf Spot, Healthy Fruit, and Healthy Leaf. The methodology includes data preprocessing, training using identical hyperparameters, and performance evaluation through accuracy and model behavior analysis during real-world testing. The findings indicate that RT-DETR achieves higher accuracy in controlled testing, reaching 90% for Anthracnose, 95% for Healthy Leaf, 100% for Healthy Fruit, and 85% for Cercospora Leaf Spot, supported by its transformer-based architecture that enhances spatial understanding. However, YOLOv8 demonstrates superior stability and consistency in real-world scenarios involving varying lighting, leaf orientations, and natural texture variations. The model also produces fewer misclassification errors, making it more reliable for practical field deployment. The implications of these results show that YOLOv8 is the most suitable model for integration into a Streamlit-based application due to its fast, responsive, and accurate inference, supporting early disease detection for chili farmers.
Co-Authors Ade Christian Adi Supriyatna Adiansyah, Rifky Ramadhan Agustinus Aditya Bintara Ahmad Yani Ahnaf putra hafezi Ardian Dwi Praba Bagus Adi Wibowo Christian , Ade DENI SETIAWAN Doni Andriansyah Erawati, Wati Erzab, Anggi Okliata Evy Priyanti Fahmi , Muhammad Fahrozi, Habil Fauziah , Sifa Gitisari, Deva Gregorius Bayu Listyoputro Imam Nawawi Indradewa, Rhian Krisnavaro Raihananta Kuncoro, Dwiyanto Mahatma Mahesa Mochamad Rizky Ainur Ridho Muhammad Fahmi Muhammad Rifki Zhaki Muhammad Rizky Heriadi Putra Mutiah, Septiany Khoirum Nihlatu Sopyani Nisrina, Restu Putri Pedro Lucio Parera Putri, Nayla Natania Rachmat Adi Purnama Raden Mohammad Riezky Pahlevi Rame Santoso Rame Santoso Rani Irma Handayani Riezky Pahlevi, Raden Mohammad Samit, Zaidan Samuel Orief Rosario Selamet Riyadi Sifa Fauziah Sujiliani Heristian Sujiliani, Sujiliani Sumarna Sumarna , Sumarna Supriyatna , Adi Syahriani Umi Nurhasanah Wati Erawati wati erawati Yani , Ahmad