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All Journal Jurnal Antropologi: Isu-Isu Sosial budaya JUITA : Jurnal Informatika Jurnal The Messenger GANDRUNG: Jurnal Pengabdian Kepada Masyarakat ALTRUISTIK : Jurnal Konseling dan Psikologi Pendidikan Komunika: Jurnal Dakwah dan Komunikasi IBDA': Jurnal Kajian Islam dan Budaya Al-Ma`mun: Jurnal Kajian Kepustakawanan dan Informasi Solidaritas: Jurnal Pengabdian Kartika: Jurnal Studi Keislaman Journal of Computing Theories and Applications Hujjah: Jurnal Ilmiah Komunikasi dan Penyiaran Islam Lentera: Jurnal Ilmu Dakwah dan Komunikasi Transaction on Informatics and Data Science Jurnal empower : jurnal pengembangan masyarakat Islam
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Department of Informatics, Faculty of Science and Technology, Universitas Islam Negeri Prof. K.H. Saifuddin Zuhri Purwokerto
Religion Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Library & Information Science Physics Social Sciences Other

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ANALISIS SEMIOTIKA ROLAND BARTHES DALAM IKLAN SHOPEE PAYLATER (BUDAYA KONSUMTIVISME DARI PERSPEKTIF KOMUNIKASI ISLAM) Ersa Balqis, Mardiani; Saefulloh, Aris; Warto
Hujjah: Jurnal Ilmiah Komunikasi dan Penyiaran Islam Vol. 9 No. 2 (2025): Hujjah: Jurnal Ilmiah Komunikasi dan Penyiaran Islam
Publisher : Universitas Nahdlatul Ulama Al Ghazali Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52802/hjh.v9i2.1853

Abstract

Abstract Consumerist culture in the digital era has been further reinforced through marketing strategies that employ financial technologies, including paylater services. One such example is the Shopee PayLater advertisement, which constructs excessive consumption as a normal aspect of modern lifestyle. This phenomenon becomes problematic from the perspective of Islamic communication, as it contradicts the principles of moderation, responsibility, and justice. This study aims to critique the consumerist ideology reproduced by the Shopee PayLater advertisement through an analysis of its denotative, connotative, and mythological meanings embedded within its visual and verbal messages. Employing a qualitative descriptive method, this study utilizes Roland Barthes' semiotic analysis and John Fiske’s theory of popular culture to interpret the visual symbols and messages represented in the advertisement. Data were collected through in-depth observation of the advertisement content and a review of literature related to consumerist ideology and Islamic communication. The analysis reveals that the advertisement normalizes indebtedness and the purchase of non-essential goods as expressions of identity and freedom. This reinforces the hegemony of digital capitalism while undermining Islamic ethical and spiritual values. Therefore, this study asserts that Shopee PayLater promotes a consumerist culture that is spiritually and socially problematic, underscoring the need for ideological critique grounded in Islamic communication principles to foster critical awareness within society. Keywords: Islamic Communication, Digital Cinsumerism, Shopee PayLater, Semiotics Abstrak Budaya konsumtif di era digital semakin menguat melalui strategi pemasaran yang memanfaatkan teknologi finansial, termasuk layanan paylater. Salah satu contohnya adalah iklan Shopee PayLater yang mengonstruksi praktik konsumsi berlebihan sebagai gaya hidup modern yang normal. Fenomena ini menjadi problematik dalam perspektif komunikasi Islam karena bertentangan dengan ajaran kesederhanaan, tanggung jawab, dan keadilan. Penelitian ini bertujuan untuk mengkritisi ideologi konsumtif yang direproduksi oleh iklan Shopee PayLater melalui analisis makna denotatif, konotatif, dan mitos yang dibangun dalam pesan visual dan verbalnya. Penelitian ini menggunakan metode desktiptif kualitatif dengan analisis semiotika Roland Barthes dan teori budaya populer John Fiske untuk menafsirkan makna visual, simbol, serta pesan yang direpresentasikan dalam iklan. Data dikumpulkan melalui pengamatan mendalam terhadap konten iklan dan literatur terkait ideologi konsumerisme dan komunikasi Islam. Hasil analisis menunjukkan bahwa iklan tersebut menormalisasi perilaku berutang dan membeli barang tidak esensial sebagai ekspresi identitas dan kebebasan. Hal ini memperkuat hegemoni kapitalisme digital dan melemahkan nilai spiritual dan etika Islam. Oleh karena itu, penelitian ini menegaskan bahwa Shopee PayLater mempromosikan budaya konsumtif yang problematis secara spiritual dan sosial, sehingga diperlukan kritik ideologis berbasis komunikasi Islam untuk membangun kesadaran kritis masyarakat. Kata Kunci: Komunikasi Islam, Budaya Konsumtif, ShopeePayLater, Semiotika
Kajian Komunikasi Interpersonal Orang Tua-Anak Dalam Perspektif Psikologi Komunikasi Kholis, Nur; Warto
Kartika: Jurnal Studi Keislaman Vol. 6 No. 1 (2026): Kartika: Jurnal Studi Keislaman (Februari)
Publisher : Lembaga Pendidikan Tinggi Nahdlatul Ulama (LPT NU) PCNU Kabupaten Nganjuk

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59240/kjsk.v6i1.507

Abstract

This study examines the role of Artificial Intelligence (AI) as a learning companion for children and its impact on parent–child interpersonal communication from a communication psychology perspective. Employing a qualitative approach, the study involved in-depth interviews with 25 parents who actively use AI-based learning applications, along with content analysis of five AI learning platforms. Data were collected between August and October 2024 in the Jabodetabek region through semi-structured interviews, participatory observation, and digital content analysis. The findings reveal that the integration of AI into children’s learning creates a triadic communication dynamic involving parents, children, and technology, with 92% of participants reporting that AI has become a routine component of daily learning activities. AI offers positive contributions by enhancing learning accessibility (84%), enabling personalized learning, and reducing emotional conflict between parents and children (72%). However, AI use may also reduce the intensity of direct verbal communication (68%) and increase the risk of technological dependency among children (28%). The study identifies four effective communication strategies—active co-engagement, complementary role specialization, scheduled tech-free quality time, and critical digital literacy education to support balanced AI use while maintaining emotional bonds within families
Enhancing Lung Cancer Classification Effectiveness Through Hyperparameter-Tuned Support Vector Machine Fita Sheila Gomiasti; Warto Warto; Etika Kartikadarma; Jutono Gondohanindijo; De Rosal Ignatius Moses Setiadi
Journal of Computing Theories and Applications Vol. 1 No. 4 (2024): JCTA 1(4) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.10106

Abstract

This research aims to improve the effectiveness of lung cancer classification performance using Support Vector Machines (SVM) with hyperparameter tuning. Using Radial Basis Function (RBF) kernels in SVM helps deal with non-linear problems. At the same time, hyperparameter tuning is done through Random Grid Search to find the best combination of parameters. Where the best parameter settings are C = 10, Gamma = 10, Probability = True. Test results show that the tuned SVM improves accuracy, precision, specificity, and F1 score significantly. However, there was a slight decrease in recall, namely 0.02. Even though recall is one of the most important measuring tools in disease classification, especially in imbalanced datasets, specificity also plays a vital role in avoiding misidentifying negative cases. Without hyperparameter tuning, the specificity results are so poor that considering both becomes very important. Overall, the best performance obtained by the proposed method is 0.99 for accuracy, 1.00 for precision, 0.98 for recall, 0.99 for f1-score, and 1.00 for specificity. This research confirms the potential of tuned SVMs in addressing complex data classification challenges and offers important insights for medical diagnostic applications.
Outlier Detection Using Gaussian Mixture Model Clustering to Optimize XGBoost for Credit Approval Prediction De Rosal Ignatius Moses Setiadi; Ahmad Rofiqul Muslikh; Syahroni Wahyu Iriananda; Warto Warto; Jutono Gondohanindijo; Arnold Adimabua Ojugo
Journal of Computing Theories and Applications Vol. 2 No. 2 (2024): JCTA 2(2) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.11638

Abstract

Credit approval prediction is one of the critical challenges in the financial industry, where the accuracy and efficiency of credit decision-making can significantly affect business risk. This study proposes an outlier detection method using the Gaussian Mixture Model (GMM) combined with Extreme Gradient Boosting (XGBoost) to improve prediction accuracy. GMM is used to detect outliers with a probabilistic approach, allowing for finer-grained anomaly identification compared to distance- or density-based methods. Furthermore, the data cleaned through GMM is processed using XGBoost, a decision tree-based boosting algorithm that efficiently handles complex datasets. This study compares the performance of XGBoost with various outlier detection methods, such as LOF, CBLOF, DBSCAN, IF, and K-Means, as well as various other classification algorithms based on machine learning and deep learning. Experimental results show that the combination of GMM and XGBoost provides the best performance with an accuracy of 95.493%, a recall of 91.650%, and an AUC of 95.145%, outperforming other models in the context of credit approval prediction on an imbalanced dataset. The proposed method has been proven to reduce prediction errors and improve the model's reliability in detecting eligible credit applications.
Aspect-Based Sentiment Analysis on E-commerce Reviews using BiGRU and Bi-Directional Attention Flow De Rosal Ignatius Moses Setiadi; Warto Warto; Ahmad Rofiqul Muslikh; Kristiawan Nugroho; Achmad Nuruddin Safriandono
Journal of Computing Theories and Applications Vol. 2 No. 4 (2025): JCTA 2(4) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.12376

Abstract

Aspect-based sentiment Analysis (ABSA) is vital in capturing customer opinions on specific e-commerce products and service attributes. This study proposes a hybrid deep learning model integrating Bi-Directional Gated Recurrent Units (BiGRU) and Bi-Directional Attention Flow (BiDAF) to perform aspect-level sentiment classification. BiGRU captures sequential dependencies, while BiDAF enhances attention by focusing on sentiment-relevant segments. The model is trained on an Amazon review dataset with preprocessing steps, including emoji handling, slang normalization, and lemmatization. It achieves a peak training accuracy of 99.78% at epoch 138 with early stopping. The model delivers a strong performance on the Amazon test set across four key aspects: price, quality, service, and delivery, with F1 scores ranging from 0.90 to 0.92. The model was also evaluated on the SemEval 2014 ABSA dataset to assess generalizability. Results on the restaurant domain achieved an F1-score of 88.78% and 83.66% on the laptop domain, outperforming several state-of-the-art baselines. These findings confirm the effectiveness of the BiGRU-BiDAF architecture in modeling aspect-specific sentiment across diverse domains.
Enhancing Community Preparedness for Disasters through the Safe School Program (SPAB) in Cilacap Regency Mudzrikatun Chabibah; Nurul Ngatiqoh; W. Warto
Empower : Jurnal Pengembangan Masyarakat Islam Vol. 10 No. 1 (2025)
Publisher : UIN Siber Syekh Nurjati Cirebon

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24235/empower.v10i1.19934

Abstract

This article discusses the SPAB program in increasing community preparedness in facing disasters that can occur anytime and anywhere. This research uses a qualitative descriptive method, which uses articles, journals and books as well as interviews with parties related to this research theme to obtain detailed information. The objectives of this research are 1) to determine the implementation of the Disaster Safe Unit Program (SPAB) in Cilacap Regency; 2) knowing the obstacles in implementing the disaster safe education unit program; 3) stages in implementing the disaster safe education unit program. The implementation of the disaster safe education unit program in Cilacap district has not been carried out optimally due to several obstacles that have occurred, one of which is a lack of budget, resulting in inadequate assets. There is a need to improve strategies in implementing the Disaster Safe Education Unit program so that it can run effectively and in accordance with the stated objectives
Classification of Cavendish Banana Quality using Convolutional Neural Network Ajeng Ayu Suryani; Ummi Athiyah; Yohani Setiya Rafika Nur; Warto
Transactions on Informatics and Data Science Vol. 1 No. 1 (2024)
Publisher : Department of Informatics, Faculty of Science and Technology, UIN Prof. K.H. Saifuddin Zuhri, Purbalingga, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v1i1.12191

Abstract

Indonesia's agricultural production is divided into two main categories: vegetables and fruits. The vegetable category includes shallots, garlic, chilies, mushrooms, spinach, cabbage, and potatoes. One of the fruit commodities from the fruit horticulture subsector is bananas, which are divided into several types, including ambon, plantains, Cavendish, pipit, and horn bananas. One of the bananas that has a good selling value in Indonesia is the Cavendish banana, but the selling value of the Cavendish banana is determined by the quality of the banana fruit. A classification process is necessary to find out the quality of bananas. We perform classification using one of the deep learning algorithms, namely Convolutional Neural Network. The experiment uses 1047 images, divided into 65% training data, 15% validation data, and 20% testing data by using epochs 20 times with 16 batch sizes, the accurate results obtained are 99%. The results indicate the effectiveness of the confusion matrix in identifying training data and detecting images. It can be concluded that using more training data leads to higher accuracy, as fewer image reading errors occur when fewer images are processed. This classification is expected to be able to classify bananas with good quality like the real condition.
Digital Da'wah Semiotics: Content Analysis of Artificial Intelligence on Abu Marlo Instagram Muhammad Syahrul Wafda; Warto Warto; Gilman Habibi
Lentera: Jurnal Ilmu Dakwah dan Komunikasi VOL 10, No. 01 (2026): LENTERA
Publisher : Fakultas Ushuluddin, Adab dan Dakwah, Universitas Islam Negeri Sultan Aji Muhammad Idris Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21093/lentera.v10i01.11697

Abstract

This research aims to analyze several da'wah content on Abu Marlo's Instagram account created using Artificial Intelligence (AI). The approach used is descriptive qualitative. Analysis using Roland Barthes' semiotics. In this discussion, the author analyzes several images from Abu Marlo's Instagram account using denotative, connotative, and mythic meanings. The research findings indicate that the image content used in Abu Marlo's preaching not only serves as a visual medium but also carries strong symbolic and ideological meaning. Therefore, it can be concluded that Islamic preaching through image content on Abu Marlo's Instagram account is an effective form of visual communication in conveying Islamic values contextually and attractively. This research shows that a semiotic approach can reveal layers of meaning in digital preaching content. This is also related to contributions in the field of communication in the digital era, where technological developments are currently accelerating, leading to the emergence of the term "contemporary da'wah."
Performance Evaluation of PHP Data Object and Native Database Connection for CRUD Optimization Across MySQL, PostgreSQL, and MySQLite Warto Warto; Anas Azhimi Qalban; Yusuf Heriyanto; Putra Aditya Priyono
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.28822

Abstract

This study presents an experimental performance evaluation of the PHP Data Object (PDO) compared to native database connections, specifically mysqli and pg_connect, in executing CRUD operations across three major relational database management systems: MySQL, PostgreSQL, and SQLite. The research aims to determine the extent to which PDO’s abstraction layer influences execution efficiency, memory utilization, and scalability in database-driven applications. Using datasets of varying sizes—1.000, 10.000, 100.000, and 1.000.000 records—each CRUD operation was benchmarked under identical system configurations. The aggregated results indicate that PDO exhibits a lower overall mean execution time of 82,54 ms with a standard deviation of 31.54, compared to Native implementations at 88,22 ms with a standard deviation of 34,28. PDO also demonstrates slightly lower average memory usage, 7,29 MB, whereas Native 7,96 MB and smaller dispersion, 0,61 MB, and Native 0,83 MB across configurations. Inferential statistical analysis using a paired t-test further indicates that the observed differences between PDO and Native implementations are statistically significant (p < 0,0001) under the evaluated experimental configurations. These findings suggest that PDO can provide comparable performance efficiency while maintaining the architectural advantages of abstraction and portability in PHP-based web systems.
The Segmentation of Local Television Audiences in Central Java in the Digital Era Warto Warto
Jurnal The Messenger Vol. 11 No. 2 (2019): July-December
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/themessenger.v11i2.1278

Abstract

This theme of this article is the audiences segmentation by local television in Central Java. This study is motivated by the large amount of number of local television stations that have sprung up, as the digital era progresses. This study uses qualitative descriptive approach with the sample of five television stations, namely Banyumas TV, Semarang TV, TVKU Semarang, Simpang5 TV Pati, and also Ratih TV Kebumen. The target of the audiences segmentation demographically is the people aged 30 years and above, except TVKU Semarang, namely teenagers and students aged 15-25 years. The development in the digital era is now an opportunity to maintain the existence of local television by doing convergence of digital media. The concept of Think Global Act Local is the opposite in the local television industry to become Think Local Act Global , for the broadcasting world if they still want to exist in the digital media.
Co-Authors Achmad Nuruddin Safriandono Agus Sriyanto Ahsin, Kahfie Ahmad Ajeng Ayu Suryani Anas Azhimi Qalban Aris Saefulloh Arnold Adimabua Ojugo De Rosal Ignatius Moses Setiadi Devi Alviana Dian Kristiawan Nugroho Eli Ermawati Ersa Balqis, Mardiani Etika Kartikadarma Fathan Fathan Fauziah, Nike Mutiara Fita Sheila Gomiasti Gilman Habibi Haniati, Umi Hidayati, Okta Nurul Imam Bukhori Jutono Gondohanindijo Mokhamad Zainal Anwar Mudzrikatun Chabibah Muhammad Syahrul Wafda Muslikh, Ahmad Rofiqul Nike Mutiara Fauziah Nur Kholis Nurul Ngatiqoh Putra Aditya Priyono Rahman Latif Alfian, Rahman Latif Rindha Widyaningsih Subowo, Edy Syahroni Wahyu Iriananda Ummi Athiyah Yohani Setiya Rafika Nur Yusuf Heriyanto