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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Informatika Universitas Pamulang KOMPUTIKA - Jurnal Sistem Komputer JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Repositor INJECT Interdisciplinary Journal of Communication
Christian Sri Kusuma Aditya
Universitas Muhammadiyah Malang
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences

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Analisis Sentimen Tweet Tentang UU Cipta Kerja Menggunakan Algoritma SVM Berbasis PSO Trifebi Shina Sabrila; Yufis Azhar; Christian Sri Kusuma Aditya
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 7 No. 1 (2022): Januari 2022
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (232.782 KB) | DOI: 10.14421/jiska.2022.7.1.10-19

Abstract

Support Vector Machine (SVM) is one of the most widely used classification algorithms for sentiment analysis and has been shown to provide satisfactory performance. However, despite its advantages, the SVM algorithm still has weaknesses in selecting the right SVM parameters to optimize the performance. In this study, sentiment analysis was done with the use of data called tweets about Undang-Undang Cipta Kerja which reap many pros and cons by the people in Indonesia, especially the laborers. The classification method used in this study is the Support Vector Machine algorithm which is optimized using the Particle Swarm Optimization method for the SVM parameters selection in the hope of optimizing the performance generated by the SVM algorithm in sentiment analysis. The results of the study using 10 k-fold cross-validations using the SVM algorithm resulted in an accuracy of 92,99%, a precision of 93,24%, and a recall of 93%. Meanwhile, the SVM and PSO algorithms produce an accuracy of 95%, precision of 95,08%, and recall of 94,97%. The results show that the Particle Swarm Optimization method can overcome the weaknesses of the Support Vector Machine algorithm in the problem of parameter selection and has succeeded in improving the resulting performance where the SVM-PSO is more superior to SVM without optimization in sentiment analysis.
Sistem Pendukung Keputusan Pemilihan Lokasi Tanah Strategis di Kota Mataram Menggunakan Metode AHP-TOPSIS Dati Nafa Alfiana; Christian Sri Kusuma Aditya; Galih Wasis Wicaksono
Jurnal Repositor Vol 5 No 1 (2023): Februari 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/repositor.v5i1.1473

Abstract

Land location selection is very important for investors, businessmen, the community or for newcomers. The strategic land selection decision support system in the city of Mataram using the AHP (Analytic Hierarchy Process) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) methods has the aim of providing recommendations for strategic land locations to build shops, shopping centers or boarding houses or rented houses. for investors, businessmen or newcomers. In this study, the AHP method used to determine the criteria was carried out by the AHP method while the ranking stage was carried out by the TOPSIS method. In this study using 4 criteria, namely price, location, area, risk and getting results in the first rank, namely the value of 0.792853 and the tests carried out in this study were testing the accuracy of the results with calculations using the Cross-validation and the accuracy values obtained from the combination of both methods reached 85%.
Prediksi Harga Saham Jakarta Islamic Index Menggunakan Metode Long Short-Term Memory Didih Rizki Chandranegara; Raffi Ainul Afif; Christian Sri Kusuma Aditya; Wildan Suharso; Hardianto Wibowo
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 9, No 1 (2023): Volume 9 No 1
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v9i1.57561

Abstract

Saat ini investasi sudah sangat menyebar luas dan banyak dari kita sedang melakukannya. Investasi ini berguna untuk mengatasi kebutuhan hidup dimasa mendatang yang tidak menentu. Salah satu penyebab tidak menentunya kebutuhan dimasa mendatang adalah inflasi. Salah satu contoh investasi adalah saham. Di dalam jual beli saham di Indonesia terdapat Jakarta Islamic Index (JII). JII adalah salah satu index yang ada di pasar modal Indonesia yang mengelompokkan beberapa saham yang masuk dalam kriteria syariah dan dihitung rata-rata dari harga saham – saham tersebut. Dalam berinvestasi saham, kita tidak bisa melakukan pergerakan yang sembarangan karena saham yang relatif berubah-ubah menjadi penyebab kegagalan dalam berinvestasi saham. Dengan demikian ketika melakukan investasi saham harus dilakukan analisa yang tepat. Perkembangan teknologi saat ini sangat maju dan juga dapat membantu kita dalam melakukan analisa dalam berinvestasi dengan melakukan prediksi harga. Pada penelitian ini, akan dimanfaatkan kemajuan teknologi tersebut dengan melakukan penelitian prediksi, penelitian ini dilakukan menggunakan metode Long short Term-Memory (LSTM). Model LSTM yang diusulkan dapat memperoleh performa yang cukup baik dengan hasil RMSE mencapai 5.20877667554, dan MAPE 0.08658576985.
Game Design for Mobile App-Based IoT Introduction Education in STEM Learning Indra Puja Laksana; Evi Dwi Wahyuni; Christian Sri Kusuma Aditya
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.5007

Abstract

STEM education has received considerable attention in recent years. However, developing valid and reliable assessments in interdisciplinary learning in STEM has been a challenge. Therefore, many students ranging from junior high school to university students are only familiar with the Internet of Things (IoT) from social media but do not know its concept and function in STEM learning. This is also supported by the absence of educational applications about IoT. This research aims to introduce IoT by using mobile applications. This research refers to the multimedia development method according. The data collection method in this study was carried out by means of observation and interviews randomly to high school students to university students. This data collection was carried out using the experimental method of application testing to analyze user needs from several aspects such as features, images, and fonts. This research is also supported by the existence of literature studies derived from several journals. The results show that the functions in the application can operate as expected. Based on the survey results of the application, 75.37% of respondents rated this application in the very good category and gave positive responses so that this application could be well received by users
Deteksi Konten Hoax Pada Media Berita Indonesia Menggunakan Multinomial Naïve Bayes Fatahillah Arsyad; Nur Hayatin; Christian Sri Kusuma Aditya
Jurnal Repositor Vol 5 No 4 (2023): November 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/repositor.v5i4.1539

Abstract

Hoax news is a problem that needs to be addressed in Indonesia. Launching a report from Kominfo (Ministry of Communication and Information) in 2020 alone there were 3464 hoax news detected. considering the large number, it will be very difficult to identify every news that is in Indonesia, not quickly let alone comprehensively. Therefore, it takes a tool or system that can detect the news that is spread, quickly and efficiently. With this purpose, this research was carried out, using the method used by Multinomial Naïve Bayes (MNB). In previous studies, there are still some shortcomings that can be covered by improvisation. To improvise in the classification of hoax news, the MNB method was chosen for this study. MNB itself is a type of Naïve Bayes which is often used for text analysis where data is represented in the form of a word frequency vector. as a comparison rival for MNB, Gaussian Naïve Bayes will also be brought in for this research. with a total of 994 news data sourced from turnbackhoax.id and as a comparison this study also uses data from previous research which amounted to 250 news. The results obtained by the GNB method reach 94% accuracy and the highest accuracy for the MNB method is 96% which shows MNB is better.
Low-rate distributed denial of service attacks detection in software defined network-enabled internet of things using machine learning combined with feature importance Muhammad Abizar; Muhammad Ferry Septian Ihzanor Syahputra; Ahmad Rizky Habibullah; Christian Sri Kusuma Aditya; Fauzi Dwi Setiawan Sumadi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1974-1984

Abstract

One of the main challenges in developing the internet of things (IoT) is the existence of availability problems originated from the low-rate distributed denial of service attacks (LRDDoS). The complexity of IoT makes the LRDDoS hard to detect because the attack flow is performed similarly to the regular traffic. Integration of software defined IoT (SDN-Enabled IoT) is considered an alternative solution for overcoming the specified problem through a single detection point using machine learning approaches. The controller has a resource limitation for implementing the classification process. Therefore, this paper extends the usage of Feature Importance to reduce the data complexity during the model generation process and choose an appropriate feature for generating an efficient classification model. The research results show that the Gaussian Naïve Bayes (GNB) produced the most effective outcome. GNB performed better than the other algorithms because the feature reduction only selected the independent feature, which had no relation to the other features.
Sentiment Analysis of the 2024 Presidential Candidates Using SMOTE and Long Short Term Memory Christian Sri Kusuma Aditya; Galih Wasis Wicaksono; Hilman Abi Sarwan Heryawan
Jurnal Informatika Universitas Pamulang Vol 8, No 2 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i2.32210

Abstract

Numerous political leaders participate in elections since they are a crucial component of the political process. Since electability is an issue, steps are taken to make political candidates running in general elections more electable. The media, including internet news media, has emerged as one of the key strategies for raising electability. Reader comments can be analyzed for sentiment to provide an evaluation of political figures. However, because the comments contain unstructured content, particularly in Indonesian text, it is difficult to interpret the sentiments of different comments in online news media. In this research, an analysis of public sentiment towards the 2024 presidential candidates will be carried out which is expressed through the Twitter social network. There are several stages to carry out sentiment analysis, including the stages of data collection, data preprocessing, balancing the distribution of the number of datasets, and sentiment classification using the LSTM method with word2vec feature representation. The results of this study show that the LSTM method combined with SMOTE due to the limited amount of data is able to produce a fairly good LSTM model with an average accuracy of 89.42% and a loss value of 0.24, the ideal scenario is when the accuracy is high and the loss is minimal, in which case the LSTM model only exhibits minor errors on a subset of the data. 
Implementation of Convolutional Neural Network Method in Identifying Fashion Image Christian Sri Kusuma Aditya; Vinna Rahmayanti Setyaning Nastiti; Qori Raditya Damayanti; Gian Bagus Sadewa
JUITA: Jurnal Informatika JUITA Vol. 11 No. 2, November 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i2.17372

Abstract

The fashion industry has changed a lot over the years, which makes it hard for people to compare different kinds of fashion. To make it easier, different styles of clothing are tried out to find the exact and precise look desired. So, we opted to employ the Convolutional Neural Network (CNN) method for fashion classification. This approach represents one of the methodologies employed to utilize computers for the purpose of recognizing and categorizing items. The goal of this research is to see how well the Convolutional Neural Network method classifies the Fashion-MNIST dataset compared to other methods, models, and classification processes used in previous research. The information in this dataset is about different types of clothes and accessories. These items are divided into 10 categories, which include ankle boots, bags, coats, dresses, pullovers, sandals, shirts, sneakers, t-shirts, and trousers. The new classification method worked better than before on the test dataset. It had an accuracy value of 95. 92%, which is higher than in previous research. This research also uses a method called image data generator to make the Fashion MNIST image better. This method helps prevent too much focus on certain details and makes the results more accurate.
HERBAL LEAF CLASSIFICATION USING DEEP LEARNING MODEL EFFICIENTNETV2B0 Rakha Pradana Susilo Putra; Christian Sri Kusuma Aditya; Galih Wasis Wicaksono
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.5119

Abstract

Science regarding plants has experienced significant progress, especially in the study of medicinal plants. Medicinal plants have been used in medicine and are still an important component in the world of health today. Among the various parts of the plant, the leaves are also one that can be used as medicine. However, not many people can recognize these herbal leaves directly. This is because the herbal leaves at first glance look almost the same, so it is difficult to differentiate them. The aim of this research is to classify herbal leaf images by identifying the structural features of the leaf images. The dataset in this study uses 10 classes of leaf images, namely, starfruit, guava, lime, basil, aloe vera, jackfruit, pandan, papaya, celery, and betel, where each class uses 350 images with a total of 3500 images of data. The EfficientNetV2B0 model was chosen because it has a minimalist architecture but has high effectiveness. Based on the results of research using the EffiecientNetV2B0 model, the accuracy was 99.14% and the loss value was 1.95% using test data.
Digital Discourse And Cultural Narratives: A Corpus-Based Analysis Of Coffee Tourism In Indonesia On Twitter Moch Fuad Nasvian; Hamdan Nafiatur Rosyida; Christian Sri Kusuma Aditya
INJECT (Interdisciplinary Journal of Communication) Vol. 10 No. 1 (2025)
Publisher : FAKULTAS DAKWAH UIN SALATIGA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18326/inject.v10i1.4434

Abstract

Public discourse on social media can provide marketing communication strategists with deeper insights for crafting relevant and relatable content. This study explores how Indonesian Twitter users discursively construct narratives around coffee tourism through user-generated content (UGC). Using a corpus of 37,553 tweets posted between February 2024 and January 2025, the study applies computational content analysis, including keyword-in-context (KWIC), co-occurrence mapping, and collocate analysis via Voyant Tools. The findings show that the term “kopi” (coffee) frequently appears alongside affective and experiential keywords such as “enak” (delicious), “liburan” (holiday), and “kebun” (plantation), reflecting coffee’s symbolic role in leisure, identity, and place-making. These discursive patterns highlight the shift from traditional promotion to participatory tourism storytelling. This study contributes to communication scholarship by illustrating how digital discourse reflects public meaning-making, and offers practical insights for destination branding through audience-centered content strategies rooted in cultural and emotional resonance.
Co-Authors Abdul Hadiy Dyo Fatra Adam Novrisal Ahmad Akbar Maulana Ahmad Rizky Habibullah Chandranegara, Didih Rizki Dati Nafa Alfiana Dzur Rifqi Aziz Evi Dwi Wahyuni Fatahillah Arsyad Fatimah Defina Setiti Alhamdani Gian Bagus Sadewa Gita Indah Marthasari Hamdan Nafiatur Rosyida Hardianto Wibowo Hasbi Atsqalani Hilman Abi Sarwan Heryawan Indra Puja Laksana Moch Fuad Nasvian Muhammad Abizar Muhammad Ferry Septian Ihzanor Syahputra Muhammad Robby Fathuda Nisrina Arintia Maghfiroh Nur Hayatin Nuryasin, Ilyas Qori Raditya Damayanti Raffi Ainul Afif Rakha Pradana Susilo Putra Roni Hadi Wijaya S, Vinna Rahmayanti Sabrila, Trifebi Shina Sumadi, Fauzi Dwi Setiawan Syaifuddin Syaifuddin Vera Suryani Wicaksono, Galih Wasis Wildan Suharso Yufis Azhar