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EDUMATIC: Jurnal Pendidikan Informatika
Published by Universitas Hamzanwadi
ISSN : -     EISSN : 25497472     DOI : 10.29408
Core Subject : Science, Education,
Computer Science & IT Education
EDUMATIC: Jurnal Pendidikan Informatika (e-ISSN: 2549-7472) adalah jurnal ilmiah bidang pendidikan informatika yang diterbitkan oleh Universitas Hamzanwadi dua kali setahun yaitu pada bulan Juni dan Desember. Adapun fokus dan skup jurnal ini adalah (1) Komputer dan Informatika dalam Pendidikan; (2) Model Pembelajaran dan Model TIK; (3) Pengembangan Media Pembelajaran Berbasis Teknologi Informatika; (4) Interaksi Manusia dan Komputer; (5) Sistem Informasi dan Teknologi Informasi.
Arjuna Subject : -
Articles 434 Documents
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Pemetaan Lintasan Karir Alumni Berdasarkan Analisis Cluster: Kombinasi K-Means dan Reduksi Dimensi Autoencoder Prasetyawan, Daru; Mulyanto, Agus; Gatra, Rahmadhan
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29713

Abstract

Alumni career mapping is a crucial aspect of evaluating and developing higher education programs. Cluster analysis, particularly the integration of k-means and autoencoder methods, has emerged as an effective solution for grouping complex and multi-dimensional alumni career data. This study aims to implement and assess the combination of k-means and autoencoder algorithms in alumni career mapping based on GPA, study duration, waiting time, job type, salary, job level, and field of study suitability. The autoencoder is employed to reduce dimensions, while k-means clusters alumni into groups based on the similarity of their career profiles. The data used in the cluster analysis is sourced from the tracer study. Pre-processing of the tracer study data is conducted through several stages, including cleaning, encoding, and normalization. The evaluation results indicate that the combination of k-means and autoencoder yields superior Silhouette and DBI scores. The Silhouette score with the autoencoder achieved 0.6112, while without it, the score was only 0.3956. The DBI value with the autoencoder is 0.566, whereas without it, the DBI reached 1.022. This cluster analysis effectively grouped the tracer study data into six clusters based on similarities in career profiles. The clustering results suggest that the formed clusters are more influenced by the alumni's job type and duration of study.
Analisis Sentimen Program Makan Siang Gratis di Twitter/X menggunakan Metode BI-LSTM Attaulah, Dimas Thaqif; Soyusiawaty, Dewi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29725

Abstract

The free lunch program became a widely discussed topic on social media, reflecting public opinion towards the policy. This research aims to analyze public sentiment towards free lunch program to evaluate the policy's effectiveness and understand public perception. Data was collected through web crawling techniques on the Twitter/X platform, resulting in 7,441 data. Processing stages include preprocessing, sentiment labeling using VADER, keyword visualization with wordcloud, and application of word embedding using Word2Vec. The oversampling technique is used to overcome data imbalance. Sentiment classification was developed using Bi-LSTM and evaluated with accuracy, precision, recall, and F1-score. The developed Bi-LSTM model achieved 88.75% accuracy, with 88.9% precision, 88.8% recall, and 88.8% F1-score. Analysis results show that the majority of public responses are positive or neutral, although there were negative sentiments that highlighted potential problems such as corruption and increasing national debt. These results provide insight into public opinion on the free lunch policy and demonstrate the effectiveness of the Bi-LSTM model in social media sentiment classification.
Sistem Pendukung Keputusan berbasis Vikor untuk Penyaluran Gas Lpg 3 Kg Bersubsidi Rahmadani, Nadhilla; Fauziah, Rizky; Mardalius, Mardalius
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29751

Abstract

PT. Citra Gas Nusantara always has difficulty in determining priority bases and has not done so objectively or still does so manually. So that in this condition it has an impact on uneven distribution that does not match the needs of the community. The purpose of this study is to create a decision support system for selecting priority LPG Gas bases based on VIKOR. The research method used is the waterfall method, which consists of the analysis, planning, implementation, and testing processes. Only 10 alternatives out of 31 alternative choices are used in the calculation, because only 10 alternatives are included in the priority base criteria based on four criteria: cylinder ownership, customers, discipline, and accuracy of Brimola transactions. The results of our findings are in the form of a decision support system for selecting priority bases for the distribution of 3 Kg LPG Gas which is realized in the form of a website. The results of the VIKOR technique calculation for the Aminuddin base obtained the lowest score of 0, so in this case, the Aminuddin Base is the base that must be prioritized for distribution. In VIKOR, the lowest value is the best compromise solution or the best alternative that is ranked first. The results of the black box test starting from login, the menu on the main page and logout also show that the system can run as expected and will also have an impact on increasing efficiency and effectiveness in the distribution of subsidized 3 Kg LPG Gas
Pendekatan Multi-Input dalam Deteksi Kanker Kulit: Implementasi EfficientNetV2-B2 dan LightGBM Ibad, M. Azka Khoirul; Winarno, Sri
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29771

Abstract

Skin cancer is one of the types of cancer with a high prevalence rate, so early detection is very important to increase the chances of recovery. This study aims to develop a skin cancer detection model that combines image data and tabular data using EfficientNetV2-B2 for image feature extraction and LightGBM for tabular data prediction estimation. The ISIC 2024 dataset used consists of 401,059 images of skin lesions with tabular features, including age, gender, location, diameter, and shape of the lesions. Tabular data is processed with normalization and encoding to avoid bias. Image data is also processed with augmentation techniques from kerascv. This multi-input model combines image and tabular features using concatenation techniques, with a dense layer as the final output. Our findings show that the model's accuracy and AUC value reached 96% and 98%, with success in handling class imbalance using undersampling and oversampling techniques. This study shows that the combination of images and tabular data increases the accuracy of skin cancer detection by 2%, compared to conventional CNN models, which only achieve an accuracy of around 94%. Moreover, this model offers better computational efficiency compared to conventional CNN models. The main contribution of this research is the use of multi-input that complements visual information with clinical data for more accurate and efficient skin cancer detection.

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