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All Journal Jurnal Inspiration JURNAL MEDIA INFORMATIKA BUDIDARMA Journal of Computer System and Informatics (JoSYC)
Kusrini Kusrini
Universitas Amikom, Yogyakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering Other

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Implementation Of C4.5 Algorithm For Predicting Late School Tuition Payments Using Python Victor Saputra Ginting; Kusrini Kusrini; Emha Taufiq
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol 10, No 1 (2020): Jurnal Inspiration Volume 10 Issue 1
Publisher : STMIK AKBA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v10i1.2535

Abstract

The Donation of Educational Development (SPP) School is one of the important components in implementing education, because School SPP is one of the requirements in achieving educational goals. Research conducted by Muqorobin, 2019 with the title "Optimization of the Naive Bayes Method with Feature Selection Gain for Predicting Late School Fee Payments" with Object Research conducted at SMK Al-Islam Surakarta resulted in an accuracy rate of 90%. The research was conducted by using several variables such as the amount of income, family dependents, parents 'educational background and parents' age. The research that will be carried out later will predict the late payment of School Fees by using the Dataset from the research conducted by Muqorobin, 2019 and implemented into the form of programming using the python programming language to produce prediction results. The research results obtained get an accuracy rate of 73%.
Peningkatan Akurasi Klasifikasi Sentimen Ulasan Makanan Amazon dengan Bidirectional LSTM dan Bert Embedding David Junggu Manggala Pasaribu; Kusrini Kusrini; Sudarmawan Sudarmawan
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol 10, No 1 (2020): Jurnal Inspiration Volume 10 Issue 1
Publisher : STMIK AKBA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35585/inspir.v10i1.2568

Abstract

Sudah memasuki revolusi industri 4.0 dengan infrastuktur internet semakin memadai dan biaya lebih murah mengakibatkan banyak masyarakat menggunakan layanan pada internet. Sehingga organisasi bisnis terdisrupsi untuk merambah ke media online. Seperti Amazon perusahaan e-commerce meliputi Costumer to Costumer maupun Business to Business, salah satu produk yang dipasarkan adalah makanan. Untuk menaikkan pemasukannya maka perusahaan harus mengerti kebutuhan pembeli. Sehingga dilakukan analisis sentimen konsumen namun proses ini memerlukan waktu lama sehingga dibuat secara otomatis menggunakan metode kecerdasan buatan. Dalam hasil penelitian tentang analisis sentimen pada dataset Amazon Fine Food Review menggunakan metode deep learning Bidirectional Long Short-Term Memory dengan penghasil vektor kata Bidirectional Encoder Representations from Transformers mampu menghasilkan akurasi yang lebih baik daripada menggunakan smetode machine learning Logistic Regression dengan pembobotan kata Mutual Information dan Bag of Words serta model deep learning Convolutional Neural Network dan Long Short-Term Memory dengan penghasil vektor kata Word2Vec dan GloVe pada konfigurasi ukuran embedding dan jumlah dataset paling besar yaitu 300 dan 85.000 sebesar 93 %. 
Klasifikasi Pengenalan Wajah Siswa Pada Sistem Kehadiran dengan Menggunakan Metode Convolutional Neural Network Henri Kurniawan; Kusrini Kusrini; Kusnawi Kusnawi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.5958

Abstract

The student attendance system is useful for monitoring student attendance. The current technology is technology capable of detecting an object, such as fingerprints, voice, eye retinas, and faces. The author will create a model that can be used to detect student faces. In this study the authors used a modified Convolutional Neural Network (CNN) algorithm. The complexity of the CNN designed is in accordance with the specifications of the hardware and software used. Face data is taken directly from students in class (private dataset). Recording of students' faces using a standard quality webcam camera. The images produced by each student are 126 images with a total of 20 classes (labels). Taking pictures with various angles of the face, namely from above, below, front, left side and right side. The augmentation techniques used are flip, random rotation and affine techniques to enrich the data. Regularization techniques, such as dropout are also used. This is in order to increase accuracy, speed of model training and avoid overfitting of the built model. The evaluation results with the confusion matrix on the modified Convolutional Neural Network (CNN) algorithm produce a faster model training process with 5.31 hours and accuracy reaching 97.78%, the loss value is stable at 0.1177, loss validation with the number 0.0192, with as many iterations (epochs) as 60. The resulting model will be developed on a prototype of the student attendance system.
Analisis Kombinasi Algoritma K-Means Clustering dan TOPSIS Untuk Menentukan Pendekatan Strategi Marketing Berdasarkan Background Target Audiens Nurus Sarifatul Ngaeni; Kusrini Kusrini; Kusnawi Kusnawi
Journal of Computer System and Informatics (JoSYC) Vol 5 No 2 (2024): February 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v5i2.4948

Abstract

The promotion is an annual agenda for STIMIK Tunas Bangsa Banjarnegara. The aim of this promotional activity is to attract more new students every year. On the other hand, campus promotion encounters obstacles in mapping applicant data from previous years so that considerations for new promotion policies are based on data from the school of origin of alumni or students. By using the K-Means Clustering algorithm, applicant data can be grouped according to the background represented through the school origin attribute. , parents' occupation and place of origin. Then the data is processed using DSS with the TOPSIS method to obtain priority references for marketing types for each cluster. The results of calculating the silhouette coefficient value for the five clusters obtained a score of 0.426. Meanwhile, in the ranking process using the TOPSIS method, the first rank was found in cluster 0 with a score of 0.994110. Further stages use the Decision Tree method to obtain output in the form of recommendations for promotion types for each cluster. For example, cluster 0 is recommended to use promotion types with codes P1, P2, P3, P8 and P9.
Co-Authors David Junggu Manggala Pasaribu Emha Taufiq Henri Kurniawan Kusnawi Kusnawi Nurus Sarifatul Ngaeni Sudarmawan Sudarmawan Victor Saputra Ginting