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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi English Language Teaching Educational Journal Journal of English Language and Pedagogy International Journal of Business, Humanities, Education and Social Sciences (IJBHES) Widyaparwa
Eko Setyo Humanika
Universitas Teknologi Yogyakarta
Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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IDEOLOGI PENERJEMAHAN WORDPLAY DALAM ALICE'S ADVENTURES IN WONDERLAND KE DALAM BAHASA INDONESIA Eko Setyo Humanika
Widyaparwa Vol 39, No 2 (2011)
Publisher : Balai Bahasa Provinsi Daerah Istimewa Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1569.099 KB) | DOI: 10.26499/wdprw.v39i2.34

Abstract

Penelitian ini mengkaji ideologi penerjemahan permainan kata (wordplay) dalam cerita anak Alices Adventures in Wonderland ke dalam bahasa Indonesia oleh tiga penerjemah. Karena ideologi penerjemahan bisa dilacak dari teknik penerjemahan, penelitian ini juga bertujuan untuk (1) menganalisis teknik penerjemahan wordplay dalam Alices Adventures in Wonderland ke dalam bahasa Indonesia dan (2) menganalisis ideologi yang mendasari pengambilam keputusan oleh para penerjemah. Kajian ini merupakan penelitian kualitatif deskriptif nonhipotesis. Strategi yang digunakan ialah embedded case study. Sampel dalam penelitian dicuplik secara purposif. Data dikumpulkan dengan teknik wawancara mendalam dan kajian dokumen. Teknik triangulasi sumber dan triangulasi metode digunakan untuk menjamin validitas data. Untuk menganalisis data, digunakan model analisis interaktif dari Miles dan Huberman. Penelitian ini menghasilkan dua temuan. Pertama, terdapatnya lima teknik penerjemahan wordplay yang diaplikasikan oleh penerjemah: literal (71 %), wordplay-wordplay translation (18 %), kompensasi (6 %), editorial technique (2,5 %), dan penghilangan (2,5 %). Kedua, proses pengambilan keputusan oleh para penerjemah lazimnya lebih didominasi oleh ideologi foreignizing.This research aims at exploring the ideology of the English-Indonesian translation of wordplays in Alice's Adventures in Wonderland conducted by three translators. Since translation ideology can be reflected in the techniques of the translation, the specific objectives of this research are (1) to analyze the techniques of transferring the wordplays in Alice's from English to lndonesian, and (2) to analyze the ideology of the trsnslation of the wordplays in question. This is a non-hypothetical descriptive qualitative research. The strategy used is an embedded case study. The samples of the research are drawn purposively, and the data are collected in such techniques as content analysis and in-depth interuiewing. Data and method triangulation are used to confirm data validity of this study. To analyze the data, this research applies Miles and Huberman's interactive analysis model. This research bears two findings: (1) in the translating of the wordplays the translators applied five techniques, i.e.literal translation (71%), wordplay-wordplay translation (18%), compensation (6%), editorial technique (2.5%), and deletion (2.5), and (2) the domination of the foreignizing ideology in the process of decision making in the translation.
Students’ perceptions and challenges in using writing outlines to write an exposition Radjaban, Yohanes; Humanika, Eko Setyo
English Language Teaching Educational Journal Vol. 7 No. 2 (2024)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/eltej.v7i2.11662

Abstract

Problems in developing writing often comes from creative processes in developing ideas to write. Outlines are often recommended as a tool to help students organize their thoughts and structure their writing. This study aims to find out students’ perceptions and the challenges the students encountered when writing an exposition text using provided outlines among the students of English Department of UTY. This study used mixed method approaches. Questionnaires and semi-structured interviews were applied to collect data. Respondents of this study were 20 students (10 males and 10 females) from second year students of English Literature Department of Yogyakarta University of Technology academic year 2023-2024, and through a purposive random sampling technique. A questionnaire was used to figure out students' perceptions, and semi-structured interviews were conducted to find out the challenges encountered by students when writing an exposition text using provided outlines. Nine respondents (6 females and 3 male) were selected in the interview session based on their scores, they were four students with the highest scores, three students with middle scores, and two students with low scores to be interviewed. The semi-structured interview data were analyzed qualitatively using the interactive model. The results of this study show that most students agreed that outlines help them write exposition texts, with 80% agreeing or strongly agreeing with the statement “The outline helps me write an exposition text.” However, students also reported three challenges, difficulty in developing connections between theory and research topics, with 60% indicating they found this aspect challenging, 55% found it challenging to translate their outlines into coherent exposition texts, and 30% students indicated a lack of confidence in their ability to use outlines effectively. This learning model may help students participate, engage, and enhance their confidence in writing exposition texts, and lecturers are aware of the importance of targeted instructional strategies, such as providing more examples and practice opportunities for developing connections between theory and research topics.
Shaping the future of translation careers: Student interest and the need for curriculum reform in the AI era Humanika, Eko Setyo; Radjaban, Yohanes
English Language Teaching Educational Journal Vol. 7 No. 3 (2024)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/eltej.v7i3.12016

Abstract

This study explores students’ interest in pursuing a career in translation in the rapid advancement of artificial intelligence and the growing need for translation curriculum reform. A mixed method was employed, involving 45 students from the English Literature Department at the University of Technology Yogyakarta (UTY) during the 2023-2024 academic year. Participants were selected using a stratified random sampling technique and included second-, third-, and fourth-year students. Data collection was conducted through questionnaires and semi-structured interviews. The questionnaire assessed students’ interest in translation careers, while the interview provided deeper insight, involving six respondents from the three batches, representing both positive and negative responses to the questionnaire items. The findings indicate that 26,6% of respondents are interested in a translation career, 55,6% are neutral, and 15,53% are not interested. Neutral responses were most common among second-year students and least common among fourth-year students, likely because higher-year students tend to focus more on career planning. The study also highlights the need to reform translation curricula by integrating machine translation into classroom instruction, as a computer assisted as well as automatic. The findings of this study suggest the need for further research on developing an AI-based model for teaching translation.
Examining Code-Switching in the Cindy Kaza and Mongol Stress Episode of Diary Misteri Sara Zehar, Rafika; Humaniika, Eko Setyo; Dewi, Septi Riana; Radjaban, Yohanes; Matas, Gordan
Jurnal Internasional Bisnis, Humaniora, Pendidikan dan Ilmu Sosial Vol 7 No 2 (2025): International Journal of Business, Humanities, Education and Social Sciences
Publisher : Universitas Teknologi Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46923/ijbhes.v7i2.593

Abstract

This study aims to analyze code-switching in an episode of Diary Misteri Sara, a program featured on Sara Wijayanto’s YouTube channel, specifically the episode involving Cindy Kaza and Mongol Stress. The primary objective is to identify the types and functions of code-switching present in the interactions between the Diary Misteri Sara team and their collaborators. Employing a qualitative approach, the analysis draws on Stockwell’s classification of code-switching types and Gumperz’s framework for its functions. A total of 218 instances of code-switching were identified, comprising tag switching (24 instances, 10.55%), inter-sentential switching (41 instances, 18.81%), and intra-sentential switching (154 instances, 70.64%). In terms of function, six categories were found: quotation (2 instances, 0.92%), addressee specification (32 instances, 14.68%), interjection (23 instances, 10.55%), reiteration (22 instances, 10.09%), message qualification (26 instances, 11.93%), and personalization versus objectification (113 instances, 51.83%). The results indicate that intra-sentential switching is the most frequently used type, while personalization versus objectification is the most dominant function. These findings contribute to a deeper understanding of the patterns and communicative roles of code-switching in multilingual interactions within digital media contexts.
Speech acts in Chris Rock’s Stand-Up Comedy Show "Selective outrage" Rizki, Akbar Arif; Humaniika, Eko Setyo; Dewi, Septi Riana; Matas, Gordan; Radjaban, Yohanes
Journal of English Language and Pedagogy Vol 8 No 2 (2025)
Publisher : Universitas Sarjanawiyata Tamansiswa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36597/jelp.v8i2.20412

Abstract

This qualitative study applies J.L. Austin's Speech Act Theory to analyze Chris Rock’s stand-up comedy show Selective Outrage. It focuses on identifying types of speech acts (locutionary, illocutionary, perlocutionary) and determining which illocutionary acts are most dominant. Data were collected through observation and note-taking, then analyzed using three stages: data reduction, data presentation, and conclusion. The results show that representative speech acts are the most dominant (44%), followed by expressive (35%), declarative (12%), directive (7%), and commissive (2%). This study offers insights into the use of speech acts in stand-up comedy and provides a deeper understanding of Chris Rock’s comedic style and communication strategies.
Enhancing SVM-Based Classification Performance on Indonesian Sentences through TF-IDF and Directional Augmentation Rianto, Rianto; Humanika, Eko Setyo; Untoro, Iwan Hartadi Tri
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 10 No 1 (2026)
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v10i1.25179

Abstract

Background: The distinction between standard and non-standard Indonesian sentences is traditionally well-defined, yet the ubiquity of digital communication has increasingly blurred these boundaries. This convergence introduces significant lexical ambiguity in formal contexts, complicating the performance of automated text classification systems. Objective: This study aims to enhance the robustness of Support Vector Machine (SVM) classification by addressing these linguistic irregularities through TF-IDF vectorization and a targeted directional augmentation strategy. Methods: A corpus comprising 5,394 labeled sentences was processed under a strict anti-leak grouping strategy to rigorously prevent semantic leakage between training, validation, and testing sets. To resolve decision boundary overlaps often missed by the baseline model, manual directional augmentation was applied, specifically targeting ambiguous sentence structures to enrich the training distribution and linguistic diversity. Results: The experiments demonstrated that directional augmentation significantly refined the model's decision margins. While the baseline model achieved a test accuracy of 94.39%, the augmented approach substantially improved generalization capabilities across unseen groups, elevating validation accuracy from 96.11% to 97.39% and test accuracy to 96.16%. Conclusion: These findings substantiate that structurally enriching the dataset effectively mitigates overfitting and improves sensitivity. However, given the scalability constraints of manual intervention, future research should prioritize automated augmentation techniques and contextual embeddings to handle deep linguistic nuances further.
Resolving Visual Ambiguity in Wood Grain Classification Using InceptionV3 and Label Smoothing Rianto Rianto; Sulistyo Dwi Sancoko; Eko Setyo Humanika
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): June 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7599

Abstract

Wood type classification in the timber industry is frequently hindered by the high visual similarity of grain patterns, a structural challenge that is particularly pronounced with small-scale datasets. This study systematically resolves this fine-grained visual ambiguity by developing a rigorously optimized InceptionV3 framework. Initial baseline evaluations conducted on a dataset of 800 images encompassing four wood species, namely Pterospermum javanicum (Bayur), Magnolia champaca (Cempaka), Tectona grandis (Jati), and Melia azedarach (Mindi), revealed that a standard ResNet50 architecture experienced a severe performance degradation, generating a random guess accuracy of merely 25 percent. Addressing this severe architectural inadequacy, the proposed InceptionV3 model integrates highly targeted spatial augmentations, a Dropout rate of 0.6, and L2 regularization. Furthermore, the optimization strategy deploys Stochastic Gradient Descent with Nesterov momentum and Label Smoothing to explicitly mitigate intra-class visual similarities. Consequently, the proposed framework achieved a robust overall accuracy of 89 percent, surpassing the baseline by a substantial 64 percent, alongside a macro-averaged F1 Score of 0.89. These empirical findings substantiate that specific architectural fine-tuning and advanced stochastic regularization are highly essential for capturing subtle wood-grain patterns, thereby offering a highly reliable, automated quality-control solution for the timber industry.
Peningkatan Sensitivitas Support Vector Machine Pada Klasifikasi Kalimat Baku Dan Tidak Baku Bahasa Indonesia Rianto, Rianto; Humanika, Eko Setyo; Tri Untoro, Iwan Hartadi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 3: Juni 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026133

Abstract

Kalimat dalam bahasa Indonesia dapat diklasifikasikan menjadi dua jenis, yaitu kalimat baku dan tidak baku. Kalimat baku digunakan sebagai bahasa resmi dalam acara formal, sementara kalimat tidak baku umum ditemukan dalam komunikasi sehari-hari. Perkembangan teknologi digital turut mendorong pergeseran penggunaan bahasa, sehingga banyak kalimat tidak baku muncul dalam konteks formal. Penelitian ini bertujuan untuk meningkatkan akurasi model klasifikasi kalimat baku dan tidak baku berbahasa Indonesia. Metode yang digunakan adalah supervised learning dengan Support Vector Machine (SVM) menggunakan dataset berjumlah 2.000 kalimat. Peningkatan akurasi dilakukan melalui TF-IDF Vectorizer dan augmentasi data. Hasil penelitian menunjukkan bahwa sebelum improvisasi, akurasi model mencapai 98.6% dengan total empat kesalahan klasifikasi pada kalimat baku pendek dan ambigu. Setelah improvisasi, akurasi meningkat menjadi 99.3%, dengan jumlah kesalahan total menurun menjadi tiga. Kebaruan penelitian ini terletak pada fokus klasifikasi kalimat baku dan tidak baku dalam konteks bahasa Indonesia, yang masih jarang dieksplorasi. Kontribusinya adalah menyediakan model yang dapat menjadi dasar aplikasi pemeriksa tata bahasa atau sistem penyaringan teks formal. Namun, penelitian ini memiliki keterbatasan pada augmentasi data yang masih dilakukan secara manual. Penelitian selanjutnya diharapkan dapat mengembangkan sistem augmentasi otomatis serta menambah jumlah dataset untuk meningkatkan generalisasi model dan penerapan lebih luas dalam NLP Indonesia.   Abstract Sentences in Indonesian can be classified into two types: standard and non-standard. Standard sentences are used as the official language in formal events, while non-standard sentences are used in daily conversation. The development of digital technology has contributed to the language shift, which causes many non-standard sentences to be used in standard contexts. This study aims to improve the accuracy of the classification model for standard and non-standard sentences in Indonesian. The method used is supervised learning with a Support Vector Machine (SVM) using a dataset of 2,000 sentences consisting of standard and non-standard forms. Accuracy improvement is carried out through TF-IDF Vectorizer and data augmentation. The results showed that the model accuracy reached 98.6% before improvisation, with four misclassifications in short and ambiguous standard sentences. After improvisation, the accuracy increased to 99.3%, with the number of errors decreasing to three. The novelty of this study lies in its focus on addressing sentence classification into standard and non-standard forms in the Indonesian language, which has rarely been explored compared to other NLP tasks. Its practical contribution is to provide a baseline model that can support grammar-checking tools or text filtering systems in digital platforms. However, this study has limitations in data augmentation, which still needs to be done manually. Future research is expected to develop an automatic augmentation system and increase the number of datasets to improve model generalization and broaden its application in Indonesian NLP.
Co-Authors Dewi, Septi Riana Matas, Gordan Rianto Rianto Rizki, Akbar Arif Sulistyo Dwi Sancoko Tri Untoro, Iwan Hartadi Zehar, Rafika