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Sistem Pendukung Keputusan Penentuan Sanksi Disiplin Bagi Siswa di SMP Kristen Makedonia Ngabang Menggunakan Metode Simple Multi Attribute Rating Technique (SMART) Mardonius Wandi Pratama; Albert Yakobus Chandra
Journal of Information System and Artificial Intelligence Vol. 3 No. 2 (2023): Vol. 3 No. 2 (2023): Journal of Information System and Artificial Intelligence
Publisher : Universitas Mercu Buana Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26486/jisai.v3i2.121

Discipline is very important in supporting school rules and regulations that must be obeyed by every student. Even with the existing rules, there are still students who violate these rules. With this, the school makes sanctions for violations that can have a deterrent effect on students so they don't violate school rules. Macedonia Ngabang Christian Middle School is one of the schools that has implemented sanctions against student discipline. However, for the provision of disciplinary sanctions, guidance and counseling (BK) teachers still experience problems in determining the appropriate disciplinary sanctions for students based on the violations committed. There are many methods that can assist in building a decision support system in determining disciplinary sanctions for students who violate school rules, one of these methods is the Simple Multi Attribute Rating Technique (SMART) method. The SMART method is the right method for this research because it can determine the sanctions for violations based on criteria and sub-criteria based on predetermined weights. With a decision support system, it can help guidance and counseling teachers and teachers who teach in schools in determining appropriate disciplinary sanctions against students who violate school rules and regulations.

Rancang Bangun Sistem Prediksi Penjualan Berbasis Web Dengan Metode Singe Moving Average Albahry, Azizan; Chandra, Albert Yakobus
Jurnal SITECH : Sistem Informasi dan Teknologi Vol 7, No 1 (2024): JURNAL SITECH VOLUME 7 NO 1 TAHUN 2024
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/sitech.v7i1.11892

Kepiawaian dalam memprediksi penjualan buku adalah hal yang utama untuk melipat gandakan pendapatan sebuah toko buku. Pada saat ini BukaBuku Jogjakarta belum mengimplementasikan sebuah metode peramalan atau prediksi buku untuk periode berikutnya, akibatnya toko buku mengalami kehabisan stok dan mengalami kelebihan stok mengakibatkan kerugian bagi toko buku. Maka dibuatlah rancang bangun sebuah sistem agar dapat memprediksi penjualan guna menentukan stok pada periode berikutnya atau masa yang akan datang melalui penerapan suatu metode prediksi Single Moving Average dengan 5 periode pergerakan. Dalam penelitian ini nantinya akan menghasilkan suatu sistem prediksi yang mengimplementasikan metode prediksi Single Moving Average, dengan memakai data penjualan pada bulan sebelumnya. Hal ini berguna untuk memudahkan pihak toko dalam menentukan stok buku. Hasil dari penelitian ini berdasarkan perbandingan dari 30 judul buku paling laku nilai eror terhadap prediksi penjualan buku sebesar 11,3%.

Implementasi Landbot Sebagai Penghubung Dialogflow NLP dengan Web Informasi Kepada Civitas Akademik Natalia, Fransiska; Chandra, Albert Yakobus
Jurnal SITECH : Sistem Informasi dan Teknologi Vol 7, No 1 (2024): JURNAL SITECH VOLUME 7 NO 1 TAHUN 2024
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/sitech.v7i1.11893

Saat ini banyak universitas telah menggunakan sistem Informasi web untuk menyampaikan Informasi. Namun, masih sering terjadi kendala yang dialami mahasiswa dan calon mahasiswa yaitu sistem yang masih memberikan Informasi secara general, kesulitan dalam mencari informasi terkait perkuliahan, serta unit atau kontak admin yang diperlukan untuk menangani kendala yang dihadapi calon mahasiswa baru , baik di jam kerja dan di luar jam kerja. Oleh karena itu, pada penelitian prototipe chatbot ini menggunakan sebuah platform berupa dialogflow dan landbot yang akan digunakan yaitu untuk mengatasi masalah yang dihadapi dan mempermudah para civitas akademik mendapatkan informasi. Adapun tahapan dalam pengembangan aplikasi chatbot ini yaitu analisis kebutuhan, desain, membuat prototipe, evaluasi dan rilis aplikasi. Metode yang digunakan dalam pembuatan chatbot ini yaitu Natural Language Processing (NLP).Proses pemberian pengetahuan dalam chatbot ini mencakup data terkait Informasi yang dibutuhkan sehingga apa yang ditanamkan dalam dialogflow dan landbot berguna untuk meningkatkan pemahaman konteks dan makna pertanyaan. Dalam hal ini pula, pelatihan model machine learning digunakan untuk meningkatkan keterampilan pemrosesan bahasa alami landbot. Berdasarkan hasil pengujian yang dilakukan aplikasi ini berjalan 100% dengan baik. Dalam hal ini menunjukan bahwa, chatbot ini mampu menjawab pertanyaan-pertanyaan yang diberikan, serta dapat mengenali pengetahuan yang telah diberikan sebelumnya.

KAJIAN PEMAHAMAN ARTI PENTING DIET SEHAT BAGI MAHASISWA Suryani, Chatarina Lilis; Murti, Siti Tamaroh Cahyono; Chandra, Albert Yakobus
Jurnal Edukasi Pengabdian Masyarakat Vol 3 No 1 (2024): JANUARI 2024
Publisher : FIP UNIRA MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36636/eduabdimas.v3i1.3721

Pada saat ini obesitas telah banyak menyerang orang muda. Mahasiswa adalah generasi muda penerus bangsa, harus mengetahui dan memahami cara diet sehat.Untuk mengetahui pemahaman mahasiswa terhadap diet yang sehat telah dilakukan survei dengan responden 121 mahasiswa di Daerah Istimewa Yogyakarta.Instrumen survei yang digunakan adalah kuisioner yang terdiri dari 1) pertanyaan karakteristik pribadi responen yang meliputi jenis kelamin, umur, berat badan, dan tinggi badan, 2) aktivitas responden meliputi aktivitas olah raga dan bekerja/belajar, 3) pola makan yang meliputi kebiasaan makan, konsumsi makanan siap saji serta pengetahuan tentang pangan rendah kalori, serta 4) pengetahuan tentang indeks masa tubuh dan kesehatan. Kusioner diupload secara online melalui google form sehingga mempermudah sampling. Data hasil sampling dianalisis dengan metode deskritif kuantitatif dan kualitatif. Hasil survei menunjukkan bahwa kesadaran mahasiswa untuk makan secara teratur masih rendah, sebagian besar mahasiswa melewatkan makan pagi sehingga mengubah pola makan yang dapat menyebabkan resiko penyakit degeneratif. Sebagian besar mahasiswa telah memahami arti pening aktivitas fisik baik dengan olah raga maupun jalan kaki. Namun masih perlu ditingkatkan pemahamannya terhadap cara diet yang sehat.

Analisis Perbandingan CNN dan Vision Transformer untuk Klasifikasi Biji Kopi Hasil Sangrai Leonardi, M. Anjas; Chandra, Albert Yakobus
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7732

Coffee is a globally cherished beverage, popular among various segments of society, including in Indonesia. The coffee processing procedure plays a pivotal role in determining the taste and final quality of the beverage. One crucial stage in this process is selecting the maturity level of coffee beans after the roasting process. However, determining this maturity level often faces challenges, particularly in large-scale processing contexts. This research focuses on evaluating two main approaches in classifying the maturity level of roasted coffee beans: Convolutional Neural Network (CNN) and Vision Transformer (ViT). The main issue encountered is the lack of studies comparing the effectiveness of these two methods in the context of coffee bean classification. Therefore, this study aims to fill this knowledge gap and provide better insights into which method is more suitable for this purpose. In this study, two CNN models, namely Xception and InceptionV3, as well as one ViT model, ViT-B16, are utilized. The dataset includes images of roasted coffee beans, raw coffee beans, and non-coffee bean images, with image sizes of 224 x 224 pixels. A comparison analysis is conducted based on classification accuracy and the ability of each model to capture characteristic features in coffee bean images. The experimental results show that the ViT-B16 model outperforms both CNN models with an accuracy of 99.33%, while the Xception and InceptionV3 models achieve accuracies of 96.67% and 96.00%, respectively. ViT-B16 demonstrates better capability in capturing global features in images, while CNN is more effective in detecting local features. However, both approaches face some challenges, including computational requirements and training time. In conclusion, although both methods have their own advantages, ViT-B16 offers significant potential for more accurate and efficient classification systems for images of roasted coffee beans. This research provides a crucial contribution to the development of coffee bean classification technology, which can enhance efficiency and consistency in the coffee processing industry.

Analisis Perbandingan CNN dan Vision Transformer untuk Klasifikasi Biji Kopi Hasil Sangrai Leonardi, M. Anjas; Chandra, Albert Yakobus
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7732

Coffee is a globally cherished beverage, popular among various segments of society, including in Indonesia. The coffee processing procedure plays a pivotal role in determining the taste and final quality of the beverage. One crucial stage in this process is selecting the maturity level of coffee beans after the roasting process. However, determining this maturity level often faces challenges, particularly in large-scale processing contexts. This research focuses on evaluating two main approaches in classifying the maturity level of roasted coffee beans: Convolutional Neural Network (CNN) and Vision Transformer (ViT). The main issue encountered is the lack of studies comparing the effectiveness of these two methods in the context of coffee bean classification. Therefore, this study aims to fill this knowledge gap and provide better insights into which method is more suitable for this purpose. In this study, two CNN models, namely Xception and InceptionV3, as well as one ViT model, ViT-B16, are utilized. The dataset includes images of roasted coffee beans, raw coffee beans, and non-coffee bean images, with image sizes of 224 x 224 pixels. A comparison analysis is conducted based on classification accuracy and the ability of each model to capture characteristic features in coffee bean images. The experimental results show that the ViT-B16 model outperforms both CNN models with an accuracy of 99.33%, while the Xception and InceptionV3 models achieve accuracies of 96.67% and 96.00%, respectively. ViT-B16 demonstrates better capability in capturing global features in images, while CNN is more effective in detecting local features. However, both approaches face some challenges, including computational requirements and training time. In conclusion, although both methods have their own advantages, ViT-B16 offers significant potential for more accurate and efficient classification systems for images of roasted coffee beans. This research provides a crucial contribution to the development of coffee bean classification technology, which can enhance efficiency and consistency in the coffee processing industry.

Analisis Performa Akurasi Klasifikasi Citra Jenis Sayur Salada Menggunakan Arsitektur VGG16, Xception dan NasNetMobile Nurafiya, Nurafiya; Chandra, Albert Yakobus
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7661

Salad is a type of leafy vegetable belonging to the Compositae family, genus Lactuca. It is rich in nutrients, including fiber, vitamin A, and minerals. Salad greens can cleanse the blood and fat, help people with coughs, and prevent high cholesterol, constipation, and insomnia. With the increasing population and awareness of the benefits of a balanced diet, consumer demand for lettuce has significantly increased. Consequently, farmers have expanded lettuce cultivation to meet consumer demand, which has the potential to cause errors in sorting different types of lettuce. Therefore, research focusing on the classification and detection of lettuce varieties is crucial to help farmers efficiently harvest lettuce based on its type using Convolutional Neural Network (CNN) methods, comparing three models: VGG16, Xception, and NasNetMobile. Data were directly obtained from lettuce farms and Kaggle. After pre-processing steps such as resizing and augmentation, the data were trained with various amounts, 200 epochs, and 64 batches during the architectural modeling stage. Based on the research results, the accuracy analysis with image classification of various types of lettuce concluded that using the Convolutional Neural Network (CNN) method by comparing three models VGG16, Xception, and NasNetMobile can classify each type of lettuce based on its class with high accuracy. In the tests conducted on the trained model, using an input size of 120 x 120, 200 epochs, and a batch size of 64, the NasNetMobile architecture model achieved the highest scores with an accuracy of 98.33%, precision of 97.8%, recall of 97.9%, and an F1-score of 97.8%. With these excellent accuracy values, the researchers hope that this analysis will make a significant contribution to the development of a superior and more efficient image classification system for agriculture, especially in selecting the appropriate CNN architecture.

Implementation Of Machine Learning To Determine The Best Employees Using Random Forest Method Taqwa Prasetyaningrun, Putri; Pratama, Irfan; Yakobus Chandra, Albert
IJCONSIST JOURNALS Vol 2 No 02 (2021): March
Publisher : International Journal of Computer, Network Security and Information System

In the world of work the presence of the best employees becomes a benchmark of progress of the company itself. In the determination usually by looking at the performance of the employee e.g. from craft, discipline and also other achievements. The goal is to optimize in decision making to the best employees. Models obtained for employee predictions tested on real data sets provided by IBM analytics, which includes 29 features and about 22005 samples. In this paper we try to build system that predicts employee attribution based on A collection of employee data from kaggle website. We have used four different machines learning algorithms such as KNN (Neighbor K-Nearest), Naïve Bayes, Decision Tree, Random Forest plus two ensemble technique namely stacking and bagging. Results are expressed in terms of classic metrics and algorithms that produce the best result for the available data sets is the Random Forest classifier. It reveals the best withdrawals (0,88) as good as the stacking and bagging method with the same value

Pembuatan Company Profile Berbasis Website Untuk Toko Rumboss Wahyu Setyaningsih, Putry; Yakobus Chandra, Albert
Jompa Abdi: Jurnal Pengabdian Masyarakat Vol. 4 No. 2 (2025): Jompa Abdi: Jurnal Pengabdian Masyarakat
Publisher : Yayasan Jompa Research and Development

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57218/jompaabdi.v4i2.1498

Perkembangan teknologi informasi yang pesat telah mendorong banyak pelaku usaha untuk memanfaatkan media digital dalam memperkenalkan bisnisnya kepada masyarakat luas. Toko Rumboss, sebagai salah satu usaha di bidang perdagangan umum, memerlukan sebuah media informasi digital yang dapat menampilkan identitas usaha, produk, serta layanan yang ditawarkan secara profesional dan mudah diakses. Untuk itu, dirancanglah sebuah website company profile sebagai solusi dalam menyampaikan informasi sekaligus meningkatkan citra usaha di era digital. Website company profile ini dirancang dengan menggunakan pendekatan desain yang responsif agar dapat diakses dengan baik di berbagai perangkat, baik desktop maupun mobile. Proses perancangan dilakukan melalui beberapa tahapan, mulai dari analisis kebutuhan, perancangan antarmuka, pengembangan sistem, hingga tahap pengujian. Fitur-fitur utama yang disediakan meliputi informasi profil toko, daftar produk, galeri, kontak, dan integrasi dengan media sosial. Dengan adanya website ini, diharapkan Toko Rumboss dapat memperluas jangkauan pasar, memperkuat identitas usaha secara online, dan mempermudah pelanggan dalam memperoleh informasi yang dibutuhkan.

Analysis of User Experience Usage on the Sardjito Hospital and Yogyakarta Regional Public Hospital Websites Using the User Experience Questionnaire (UEQ) Wahyu Setyaningsih, Putry; Yakobus Chandra, Albert
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1411

The user experience of healthcare websites is crucial for ensuring accessibility, usability, and engagement among diverse stakeholders, including patients, caregivers, and healthcare professionals. This study evaluates the RS Sardjito and RS Jogja websites using six key dimensions: Attractiveness, Perspicuity, Efficiency, Dependability, Stimulation, and Novelty. Both websites excel in Attractiveness and Perspicuity, showcasing visually appealing and user-friendly platforms. RS Sardjito demonstrates strength in Efficiency, enabling effective task completion, while RS Jogja outperforms in Dependability and Novelty, reflecting higher reliability and innovation. However, areas for improvement include Novelty and Dependability for RS Sardjito and Efficiency for RS Jogja, with both platforms requiring enhancements in Stimulation to deepen user engagement through interactive features. These findings offer actionable insights for driving policy development in healthcare website design and functionality, addressing key areas such as accessibility, usability, efficiency, reliability, and innovation. Policies should prioritize user-centered design principles, implement robust security measures to strengthen reliability, and encourage creative approaches to foster innovation. Additionally, regular benchmarking and user feedback mechanisms should be institutionalized to ensure continuous improvement. By systematically addressing these dimensions, healthcare organizations can optimize digital platforms to improve access to healthcare services, enhance patient engagement, and advance the overall quality of healthcare delivery, contributing to the growing body of research on healthcare website optimization and aligning user experience with organizational goals.

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