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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknologi Sistem Informasi dan Aplikasi SPIRIT JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Journal of Advanced in Information and Industrial Technology (JAIIT) Teknika DECODE: Jurnal Pendidikan Teknologi Informasi JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Informatika Teknologi dan Sains (Jinteks) Knowbase : International Journal of Knowledge in Database The Indonesian Journal of Computer Science Multidisciplinary Indonesian Center Journal
Slamet Kacung, Slamet
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Religion Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Other

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Sentiment Analysis of Alfagift Application User Reviews Using Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) Methods Damayanti, Erika; Vitianingsih, Anik Vega; Kacung, Slamet; Suhartoyo, Hengki; Lidya Maukar, Anastasia
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 4 No. 2: JULI 2024
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v4i2.478

Abstract

The rapid advancement of mobile apps has emerged as an important aspect of the routine of internet-connected users. In Indonesia, many companies are introducing their apps to improve the quality of service for users, and Alfamart is one of them. However, users have identified many shortcomings in these apps. This feedback is provided by users on the review feature of the Alfagift app on the Google Play Store. This research aims to apply a sentiment analysis approach to identify the application's shortcomings so that developers can understand the aspects that need to be improved to improve the quality of application services. The research stages include data collection, preprocessing, labeling, weighting, classification of LSTM and SVM methods, and performance evaluation using a confusion matrix. The dataset consists of 1000 reviews obtained through web scraping techniques. This research uses the Lexicon-based method to classify the dataset into positive, negative, and neutral categories. The analysis results show that 801 data are classified as positive sentiment, 77 as negative sentiment, and 122 as neutral sentiment. Based on testing, both SVM and LSTM methods show good performance. The best accuracy results were obtained using the SVM method, which amounted to 83.5%. Meanwhile, the LSTM method achieved an accuracy of 82%.
Desain dan Implementasi Sistem Informasi Usaha Mikro Kecil Menengah dan Pariwisata Desa Tambak Kalisogo Muzakki, Achmad; Kacung, Slamet; Nasrullah, Muhammad; Larisa, Damasya Ine; Poetra, Akbar Rizki
Journal of Advances in Information and Industrial Technology Vol. 5 No. 2 (2023): Nov
Publisher : LPPM Telkom University Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52435/jaiit.v5i2.423

Abstract

Desa Tambak Kalisogo adalah desa yang terletak di Kecamatan Jabon, Kabupaten Sidoarjo. Desa Tambak Kalisogo memiliki warga yang mayoritas mata pencahariannya adalah nelayan yang hasil tangkapannya masih dipasarkan dan dikonsumsi di daerah setempat. Desa ini memiliki potensi sebagai daerah pariwisata karena terletak dekat dengan lautan serta memiliki beberapa kolam pemancingan ikan, kesenian pewayangan silat jasawisogo. selain itu, Desa Tambak Kalisogo mempunyai dan produk usaha mikro kecil menengah (UMKM) seperti, rosella dan bidaran mujahir. Namun, permasalahan Desa Tambak Kalisogo muncul dari perkembangan pariwisata nasional dan transformasi digital usaha mikro kecil menengah, yaitu hasil tangkapannya nelayan agar bisa dikelola secara mandiri, bukan dijual ke tengkulak setempat sehingga menjadi produk unggulan usaha mikro kecil menengah (UMKM), Minimnya media informasi yang mengenalkan potensi Desa Tambak Kalisogo, sehingga banyak pariwisata yang tidak mengetahui dan tidak berkunjung.
Application of Faster R-CNN Deep Learning Method for Rice Plant Disease Detection Pujiono, Halim; Vitianingsih, Anik Vega; Kacung, Slamet; Lidya Maukar, Anastasia; Fitri Ana Wati, Seftin
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 8 No. 2 (2024)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v8i2.1165

Abstract

Plant diseases, particularly in staple crops like rice, significantly affect the stability of rice production in Indonesia. Crop failure caused by rice plant diseases present a critical challenge for farmers.  Early diagnosis is crucial for preventing and managing rice diseases, as it facilitates more effective preventive measures, reduces yield losses, and boosts overall agricultural production. This study aims to apply the Faster Region Convolutional Neural Network (Faster R-CNN), a deep learning approach, to detect rice plant diseases. The Grid Search method was employed as a hyperparameter tuning technique to identify the optimal parameter combination for enhancing algorithm performance. Experimental results demonstrate the model's performance, achieving an accuracy rate of 88%, recall and precision of 100%, and an F1 Score of 93%. These findings indicate that the Faster R-CNN method effectively recognizes and classifies rice plant diseases with a high degree of accuracy.
Sentiment Analysis of Brand Ambassador Influence on Product Buyer Interest Using KNN and SVM Putri, Natasya Kurnia; Vitianingsih, Anik Vega; Kacung, Slamet; Maukar, Anastasia Lidya; Yasin, Verdi
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.29469

Abstract

In the dynamic marketing, companies usually use strategies involving celebrities or influencers to promote their products or brands. The currently popular strategy is using Korean boy bands as brand ambassadors. This collaboration certainly gets a lot of opinion responses through tweets on X app social media. This research aims to analyze sentiment to determine how the product buyer's interest responds to brand suitability, brand image management, and the influence of issues that arise in this collaboration. The research stages consist of data collection, pre-processing, labeling, weighting, and classification with K-Nearest Neighbor and Support Vector Machine and performance evaluation using a confusion matrix. The dataset used was 696 tweets taken using web scrapping techniques. This research uses the Lexicon-based method to divide the dataset into positive, negative, and neutral classes. The SVM method shows superior test results by achieving an accuracy rate of 83.34% compared to the KNN method, which produces an accuracy value of 71.2% in its calculations
Analysis of A Priori Algorithm in Medical Data for Heart Disease Identification with Association Rule Mining Sutejo, Davip; Yudha Adhi Jaya, Villa Indra; Kacung, Slamet
Knowbase : International Journal of Knowledge in Database Vol. 4 No. 2 (2024): December 2024
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v4i2.8909

Abstract

Heart disease is one of the leading causes of death worldwide, so it is important to identify risk factors that can contribute to the development of this disease in order to carry out early prevention. This study aims to identify patterns of association between risk variables and the incidence of heart disease using the Association Rule Mining (ARM) method combined with the A priori algorithm. The data used in this study includes lifestyle information, medical history, and other health parameters, obtained from the UCI Machine Learning repository. The analysis results showed that with a support value between 30% and 70%, the strongest association rule was found between sex (sex = 1) and angina (exang = 1), with a lift value of 1.67, indicating a strong positive relationship towards a positive diagnosis (target = 1). In addition, other moderate association rules were found, such as the combination of cp_1 = 1 and ca_0 = 1, with a lift value of about 0.73, indicating a weaker association. These findings suggest that some attribute combinations have higher predictive power, which can be used to improve prediction models in the medical diagnosis of heart disease. This research also highlights the main challenges faced by the A priori algorithm, such as computational complexity and selecting the right threshold to obtain significant rules
Sentiment Analysis On Tripadvisor Travel Agent Using Random Forest, Support Vector Machines, and Naïve Bayes Methods Fauzi, Ariq Ammar; Vitianingsih, Anik Vega; Kacung, Slamet; Maukar, Anastasia Lidya; Wati, Seftin Fiti Ana
Teknika Vol. 14 No. 1 (2025): March 2025
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v14i1.1198

Abstract

TripAdvisor faces problems in improving the quality of service on its application, namely the presence of unexpected or non-functional features, which can affect the user experience and reduce trust in the application.  This research aims to develop an application capable of performing sentiment analysis on TripAdvisor application user reviews on the Google Play Store with negative, positive, and neutral classifications using the Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB). The RF method was chosen in this study because of its ability to handle large and complex data very accurately, while SVM is able to classify data on a large scale and is resistant to overfitting, while NB is able to classify text with clear probabilities. The Lexicon-based method as data labelling. The results of sentiment analysis from 1500 reviews with web scrapping show the classification of positive, negative, and neutral sentiments of 48, 726, and 646 data, respectively. Model performance in RF, SVM, and NB testing gets an accuracy value of 94%, 93.6%, and 77.8%, respectively. The RF model produces the best accuracy compared to other methods. The RF model produces the best accuracy compared to other methods. The results of sentiment analysis from 1500 user reviews allow developers to identify features that are often criticized or do not function properly in their application services.
Analisis dan Perancangan Sistem Informasi Manajemen Pegawai Menggunakan Metode Waterfall Berbasis Web Vitianingsih, Anik Vega; Fardhan Maulana, Abelardi; Kacung, Slamet; Lidya Maukar, Anastasia; Wati, Seftin Fitri Ana
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 5 No 2 (2024)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.5.2.237

Abstract

In today's digital era, the need for an employee management information system is increasingly urgent. Organizations must be able to overcome the complex challenges of managing their human resources to remain competitive in the ever-changing market. With the right Personnel Information System, organizations can optimize the management of personal, performance, and administrative information of their employees efficiently. One important aspect of a Personnel Information System is the mapping of workforce needs, which enables organizations to plan appropriate employee recruitment and development strategies. In addition, efficient scheduling is also a key focus, as proper placement and wise resource allocation can improve overall productivity. However, manually managing employee data is no longer sufficient in this digital age. Errors, delays, and loss of information often occur in manual processes, causing losses in terms of both time and finances. Therefore, the implementation of a robust and efficient Personnel Information System is a must. The Waterfall method, with its structured step-by-step approach, was able to provide clear guidance in the development of this system. A comprehensive analysis stage ensures that the needs of the organization are well understood, while the design stage guarantees that the system design meets the right specifications. With the results of this study, it is expected that organizations will be able to develop a Personnel Information System that suits their needs, improve the efficiency of human resource management, and optimize overall performance. Thus, the Personnel Information System is not only an administrative tool, but also one of the key factors in organizational success in this digital era.
PREDIKSI HARGA SAHAM PT TELKOM MENGGUNAKAN METODE CNN-LSTM Pratama, Ferdian Rizki; Santoso, Budi; Kacung, Slamet
Journal of Information System Management (JOISM) Vol. 7 No. 1 (2025): Juni
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/joism.2025v7i1.2087

Abstract

PT Telkom sebagai perusahaan informasi dan komunikasi terbesar di Indonesia, memiliki harga saham yang menarik minat investor. Penelitian ini bertujuan untuk mengembangkan aplikasi yang mampu memprediksi harga saham PT Telkom menggunakan metode CNN-LSTM. Tantangan dalam memprediksi harga saham meliputi volatilitas pasar, keterbatasan data historis, dan kompleksitas faktor-faktor yang mempengaruhi harga. Metode CNN digunakan untuk mengenali pola spasial dalam data, sementara LSTM mengatasi masalah vanishing gradient dan menangkap dependensi jangka panjang. Model CNN-LSTM diuji dengan berbagai kombinasi hyperparameter, termasuk learning rate (0.001, 0.0001, dan 0.0005), kernel size (3, 5, dan 7), dan jumlah epoch (30, 50, dan 100). Hasil terbaik diperoleh dengan konfigurasi learning rate 0.0005, kernel size 7, dan 100 epoch, yang menghasilkan nilai Mean Absolute Error (MAE) sebesar 56.13, Root Mean Squared Error (RMSE) sebesar 75.75, dan koefisien determinasi (R²) sebesar 0.973. Hasil ini menunjukkan kemampuan prediksi yang baik dari model. Penelitian ini diharapkan dapat memberikan solusi bagi investor dalam memprediksi harga saham PT Telkom dan membantu pengambilan keputusan investasi.
SISTEM DETEKSI PENYAKIT PADA DAUN TANAMAN KENTANG MENGGUNAKAN METODE CNN ARSITEKTUR VGG-Net Prasetyo, Prasetyo Tri Utomo; Santoso, Budi; Kacung, Slamet
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 2 (2025): EDISI 24
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i2.5758

Abstract

Deteksi dini terhadap penyakit pada daun tanaman kentang memiliki peran krusial dalam mencegah penurunan produktivitas hasil panen. Penyakit seperti early blight dan late blight dapat dikenali melalui karakteristik visual pada permukaan daun, namun identifikasi secara manual cenderung bersifat subjektif dan memerlukan waktu yang cukup lama. Penelitian ini bertujuan untuk merancang sistem deteksi otomatis penyakit daun kentang dengan memanfaatkan metode Convolutional Neural Network (CNN) melalui pendekatan transfer learning menggunakan arsitektur VGG16. Dataset yang digunakan mencakup tiga kelas, yaitu daun sehat (healthy), early blight, dan late blight. Tahapan pra-pemrosesan meliputi preprocessing citra, augmentasi data, serta pelatihan model dengan memanfaatkan bobot awal dari VGG16. Parameter pelatihan yang diterapkan antara lain batch size sebesar 32, learning rate sebesar 0,0001, dimensi gambar 224×224 piksel, dan jumlah epoch sebanyak 10. Berdasarkan hasil pengujian, model mampu mencapai akurasi sebesar 95%, disertai nilai precision, recall, dan F1-score yang tinggi dan konsisten untuk setiap kelas. Evaluasi menggunakan confusion matrix menunjukkan performa klasifikasi yang baik dengan tingkat kesalahan prediksi yang rendah. Dengan demikian, sistem ini memiliki potensi untuk diterapkan sebagai alat bantu bagi petani dalam melakukan identifikasi penyakit daun secara cepat dan akurat di lapangan.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN E-LIQUID VAPOR MENGGUNAKAN METODE MOORA Fadilla, Muhammad Myrza; Kacung, Slamet; Santoso, Budi
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 2 (2025): EDISI 24
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i2.5775

Abstract

Meningkatnya popularitas rokok elektrik di Indonesia diikuti dengan pertumbuhan ragam produk e-Liquid yang beredar di pasaran, menciptakan tantangan bagi konsumen dalam memilih e-Liquid yang sesuai dengan kebutuhan mereka. Penelitian ini bertujuan untuk mengembangkan sistem pendukung keputusan pemilihan e-Liquid Vapor menggunakan metode MOORA (Multi-Objective Optimization by Ratio Analysis). Metode penelitian meliputi pengumpulan data melalui wawancara dengan pengguna vapor dan pemilik vape store untuk mengidentifikasi kriteria penilaian, yang selanjutnya diimplementasikan dalam sistem pendukung keputusan dengan empat kriteria utama: rasa (50%), kadar nikotin (25%), harga (15%), dan ketersediaan stok (10%). Hasil penelitian menunjukkan bahwa metode MOORA menghasilkan rekomendasi e-Liquid yang sesuai dengan preferensi pengguna. Dari tujuh alternatif e-Liquid yang dianalisis, lima alternatif direkomendasikan berdasarkan nilai optimasi tertinggi dengan alternatif A6 (Fruity, 9mg, >Rp.150.000, mudah didapatkan) memperoleh peringkat tertinggi dengan nilai optimasi 0,076. Sistem ini memberikan solusi efektif bagi konsumen, terutama pengguna baru, dalam membuat keputusan pemilihan e-Liquid yang lebih tepat sesuai dengan kriteria yang diinginkan.
Co-Authors Achmad Muzakki Agustinus Noertjahyana Anastasia Lidya Maukar Anik Vega Vitianingsih Damayanti, Erika Dwi Budi Santoso DWI CAHYONO Fadilla, Muhammad Myrza Fardhan Maulana, Abelardi Fauzi, Ariq Ammar Fauzi, Chamdan Fitri Ana Wati, Seftin Hariyanti, Yenny Hengki Suhartoyo, Hengki Hermansyah, David Kesuma Wardanie, Debleng Puja Khoirul Umam Lambang Probo Sumirat, Lambang Probo Larisa, Damasya Ine Lidya Maukar, Anastasia Maulidiana, Putri Dwi Rahayu Muhammad Nasrullah Nugraha, Azariz Ananta Leo Pamudi Pamudi, Pamudi Pangestu, Resza Adistya Poetra, Akbar Rizki Prasetyo, Prasetyo Tri Utomo Pratama, Ferdian Rizki Pujiono, Halim Putri, Natasya Kurnia Sutejo, Davip Warsito Sujatmiko, Achmad Wati, Seftin Fiti Ana Wati, Seftin Fitri Ana Wijiono, Aditya Kusuma Yasin, Verdi Yudha Adhi Jaya, Villa Indra