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MODALITAS DALAM TEKS PIDATO PUTIN MEREKA MENGERTI PERANG NUKLIR DENGAN ANALISIS PENDEKETAN HALLIDAY & MATTHIESSEN (2004) SERTA FAIRCLOUGH (SANTOSA, 2012) Risal; Ainur Rohma Husni; Muhammad Raihan Popoy Hakim; Bima Kurniawan
Jurnal Media Akademik (JMA) Vol. 2 No. 7 (2024): JURNAL MEDIA AKADEMIK Edisi Juli
Publisher : PT. Media Akademik Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62281/v2i7.661

Abstract

Penelitian ini menganalisis penggunaan modalitas dalam teks pidato Putin terkait dengan perang nuklir. Analisis modalitas dilakukan menggunakan teori Halliday dan Matthiessen (2004) dan Fairclough (Santosa, 2012) yang membagi modalitas menjadi dua, yaitu modalitas dalam bentuk modalisasi (modalization) yang dibagi menjadi dua bagian Probability (certain, possible, perhaps, etc.) dan Frequency (Always, Usual, Sometimes, etc.). Hasil penelitian menunjukkan bahwa Putin menggunakan modalitas seperti "akan" dan "harus" . Modalitas seperti "selalu" dan "biasanya" digunakan untuk menunjukkan kebiasaan dalam menerima tamu negara. Analisis ini juga menunjukkan bahwa modalitas berfungsi sebagai unsur penting dalam pidato Putin untuk menunjukkan kesiapan dalam menerima tamu.
ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI MINAT SISWA DALAM PENGAMBILAN KEPUTUSAN MEMILIH PROGRAM STUDI AKUNTANSI UNIVERSITAS PANCA BHAKTI DI KOTA PONTIANAK Nadia, Christine; Risal; Wulandari Chairina, Septi; Mayasafitri, Rina
Jurnal Akuntansi, Auditing dan Investasi Vol 4 No 1 (2024): JURNAL AKUNTANSI, AUDITING & INVESTASI
Publisher : Program Studi Akuntansi Universitas Panca Bhakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54035/jaadi.v4i1.459

Abstract

This research aims to determine the influence of: (1) Study program image on students' interest in choosing the Panca Bhakti University Accounting Study Program, (2) Education costs on students' interest in choosing the Panca Bhakti University Accounting Study Program, (3) Environmental factors on students' interest in choosing the Study Program Panca Bhakti University Accounting, (4) Level of Difficulty regarding student interest in choosing the Panca Bhakti University Accounting Study Program. The research respondents were high school and vocational school students in class XII in Pontianak with a total of 99 respondents. Analysis testing includes validity test, reliability test, normality test, heteroscadesity test, multicollinearity test. The data analysis technique used is multiple linear regression analysis. The research results show that the study program image variable (X1) has a significant level (0.310>0.05), so the study program image variable has no effect on student interest. Then for the Education Cost variable (X2) with a significant level (0.724>0.05), the Education Cost variable has no effect on Student Interest. Then for the Environmental Factor variable (X3) with a significant level (0.000<0.05), the Environmental Factor variable has a significant effect on Student Interest. And for the Difficulty Level variable (X4) with a significant level (0.403<0.05), the Difficulty Level variable has no effect on Student Interest. Based on the results of the F test with a significance level of 5%, the sig value is 0.000 < 0.05, it can be concluded that the variables X1 study program image, majoring in accounting at Panca Bhakti University Pontianak (Y).
PELATIHAN GURU DAN TANTANGAN BEBRAS 2024 UNTUK PENGENALAN COMPUTATIONAL THINKING DI BIRO BEBRAS MARANATHA Wijanto, Maresha Caroline; Toba, Hapnes; Ayub, Mewati; Karnalim, Oscar; Tan, Robby; Natasya, Rossevine Artha; Senjaya, Wenny Franciska; Adelia; Edi, Doro; Bunyamin, Hendra; Kasih, Julianti; Yulianti, Diana Trivena; Widjaja, Andreas; Johan, Meliana Christianti; Surjawan, Daniel Jahja; Zakaria, Teddy Marcus; Risal; Kandaga, Tjatur
Jurnal Abdimas Ilmiah Citra Bakti Vol. 6 No. 2 (2025)
Publisher : STKIP Citra Bakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38048/jailcb.v6i2.5237

Abstract

Pemahaman siswa terhadap konsep Computational Thinking (CT) masih tergolong rendah, sementara pengenalan terhadap CT menjadi krusial di era digital saat ini. Tantangan Bebras menjadi sarana edukatif yang efektif untuk memperkenalkan CT melalui berbagai soal (Bebras task) yang bersifat aplikatif dan menantang. Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan pemahaman dan keterlibatan siswa dalam CT melalui pembekalan guru dan pelaksanaan Tantangan Bebras 2024. Mitra kegiatan adalah guru dan siswa dari jenjang SD, SMP, dan SMA yang tergabung dalam Biro Bebras Maranatha. Metode yang digunakan meliputi lokakarya nasional, pelatihan guru, technical meeting, pelaksanaan Tantangan Bebras, dan evaluasi prestasi siswa. Hasil menunjukkan peningkatan partisipasi peserta sebanyak 4.429 siswa dari 136 sekolah, meningkat signifikan dibanding tahun sebelumnya. Sebanyak 165 siswa berhasil meraih peringkat 1–6, dengan sebagian besar berasal dari sekolah yang mengikuti Gerakan PANDAI. Evaluasi juga menunjukkan bahwa pembekalan guru efektif meningkatkan kesiapan dalam mengenalkan CT kepada siswa. Kegiatan ini menunjukkan bahwa kolaborasi antara pelatihan guru dan Tantangan Bebras dapat menjadi strategi efektif untuk memperluas pemahaman dan kemampuan siswa dalam CT.
PENERAPAN ALGORITMA K-NEAREST NEIGHBOR DALAM ANALISIS PEMINJAMAN BARANG PADA DIVISI INVENTARIS TVRI MAKASSAR Risal; Danuputri, Chyquitha; Darniati; AM Hayat, Muhyiddin
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v17i2.474

Abstract

Inventory management in the TVRI Makassar Inventory Division is inefficient due to the lack of a predictive system, hampering proactive asset requirement planning. This study aims to apply the K-Nearest Neighbor (KNN) algorithm to analyze historical borrowing patterns, predict demand for goods three months in advance, and evaluate model accuracy. Using a quantitative approach, this study implements a systematic machine learning workflow, including data preprocessing, temporal feature engineering, class imbalance handling using the Synthetic Minority Over-sampling Technique (SMOTE), and hyperparameter optimization using GridSearchCV. The results show that the optimized KNN model achieved an overall accuracy of 80.18%, significantly outperforming the baseline model. Key findings revealed that the model's performance is contextual, with very high reliability (F1-Score > 0.95) on frequently borrowed assets, and is able to identify strong temporal demand patterns. It is concluded that KNN is effective for segmented inventory demand prediction and has the potential to serve as a basis for TVRI Makassar to adopt a proactive, data-driven inventory management strategy, enabling more efficient resource allocation.