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THE EFFECTIVENENESS OF USING PIAGET, VYGOTSKY, AND BRUNNER THEORIES IN TEACHING ENGLISH FOR YOUNG LEARNERS AT SDN KEBUN BUNGA 6 BANJARMASIN Muthmainnah, Inna; Izzatil, Nor; Nor, Hidayah
Tarbiyah : Jurnal Ilmiah Kependidikan Vol 8, No 1 (2019): Januari-Juni
Publisher : Universitas Islam Negeri Antasari Banajarmasin

To address three theories in teaching English for young learners (Piaget, Vygotsky, and Brunner), the researchers believe that an effective teaching teachniques that pay attention not only to the needs of the young learners but also to their interests are very important. This research reported our pre-experimental study on the effectiveness of using Piaget, Vygotsky, and Brunner theories in teaching English for young learners at SDN Kebun Bunga 6 Banjarmasin. There were 26 students of the fourth grade, study English at SDN Kebun Bunga 6 Banjarmasin, participated in this research. The data collected by conducting three times treatment in one class of the fourth grade, and written tests (pre-test and post-test). Findings revealed that the students got better score after the implementation of those three theories (Piaget, Vygotsky, and Brunner) in the classroom; they were very enthusiastic learned English in the classroom by interacting with the teachers and the peers. Their responses toward this activity were also positive. This research is expected to be beneficial for teachers who are interested in applying those three theories in English classroom, especially for young learners? classroom.

Penerapan Algoritma Random Forest dalam Prediksi Kelayakan Air Minum Abdi, Khairul; Warjaya, Angga; Muthmainnah, Inna; Pahutar, Padli Husaini
Jurnal Ilmu Komputer dan Informatika Vol 3 No 2 (2023): JIKI - Desember 2023
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jiki.81

Tantangan meningkatnya kebutuhan akan air bersih di tengah ketersediaan yang terbatas mendorong penelitian ini. Pencemaran air, terutama oleh limbah rumah tangga, menyebabkan penurunan kualitas air dan kelangkaan sumber air bersih. Dampak buruknya terhadap kesehatan masyarakat, terutama diare, menunjukkan urgensi identifikasi dini kualitas air yang tidak layak konsumsi. Oleh karena itu, penelitian ini menerapkan algoritma Random Forest untuk klasifikasi kualitas air dan prediksi kelayakan air minum. Penggunaan teknik data mining, khususnya Random Forest, diharapkan dapat mengidentifikasi pola kompleks dalam data dan faktor-faktor yang mempengaruhi kualitas air. Menggunakan dataset Water Quality dari Kaggle, hasil penelitian menunjukkan akurasi sebesar 69%. Analisis Feature Importance Score memperlihatkan kontribusi relatif fitur terhadap prediksi. Kurva ROC menggambarkan optimalitas klasifikasi, sementara Confusion Matrix memberikan gambaran kinerja model. Confusion Matrix yang merinci hasil klasifikasi model Random Forest Classifier. Diagonal utama menunjukkan jumlah instance yang benar-benar diprediksi dengan benar untuk kategori "Potabel" dan "Tidak Potabel", masing-masing 370 dan 84. Namun, terdapat 160 instance yang salah diklasifikasikan sebagai "Potabel" dan 42 sebagai "Tidak Potabel".

ANALISIS PERBANDINGAN TINGKAT AKURASI ALGORITMA CNN DAN SVM DALAM KLASIFIKASI PADA DAUN GEDI, DAUN PEPAYA DAN DAUN UBI Mulyana, Sri; Muthmainnah, Inna
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 4 (2024): JATI Vol. 8 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i4.10144

Daun merupakan salah satu aplikasi penting dalam pengenalan pola dan pengolahan citra. Identifikasi dan klasifikasi daun dapat digunakan dalam berbagai bidang, seperti pertanian, penginderaan jauh, dan penelitian lingkungan. Penelitian ini mengeksplorasi perbandingan tingkat akurasi antara dua algoritma klasifikasi, yaitu Convolutional Neural Network (CNN) dengan arsitektur VGG16 dan Support Vector Machine (SVM), dalam konteks klasifikasi daun Gedi, daun Pepaya, dan daun Ubi. Data citra daun dikumpulkan dan dibagi menjadi kelompok pelatihan, validasi, dan pengujian. CNN diimplementasikan dengan arsitektur VGG16 untuk mengeksplorasi potensi penggunaan arsitektur yang lebih kompleks untuk memahami fitur daun. Hasilnya menunjukkan tingkat akurasi yang sangat baik untuk CNN, mencapai 100%, sedangkan SVM memiliki tingkat akurasi 44,07%.

The Antibacterial Potential of Ethanol Extracts of Torch Ginger Leaves (Etlingera elatior (Jack) R.M.Sm.) Against Enterococcus faecalis as an Alternative Irrigation Material in Root Canal Treatment Amalia Bachtiar, Zulfi; Luthfiani, Luthfiani; Muthmainnah, Inna; Putra Manurung, Supredo
Jurnal Kesehatan Gigi Vol 11, No 2 (2024): Desember 2024
Publisher : Jurusan Kesehatan Gigi, Poltekkes Kemenkes Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31983/jkg.v11i2.11441

Root canal infection is a polymicrobial infection that is a progression of chronic caries affecting the dental nerve tissue and surrounding root tissue, which can be accompanied by extremely uncomfortable sharp pain. Enterococcus faecalis is one of the infecting agents that can be found in primary root canals experiencing endodontic infections and is resistant to root canal irrigation materials, thus leading to root canal treatment failure. Herbal plants have been used since ancient times by the Indonesian people to address health issues due to the believed beneficial properties of their constituents. Research on torch ginger reports pharmacological activities such as antimicrobial, antioxidant, anti-inflammatory, anticancer, and anti-aging effects. Torch ginger (E. elatior) is a spice plant reported to have antibacterial bioactive compounds. This study aims to determine the antibacterial activity of ethanol extract from torch ginger leaves (E. elatior) against Enterococcus faecalis bacteria. The Kirby-Bauer disk diffusion method was used to determine the minimum inhibitory concentration (MIC) value and the streaking method from MIC testing to determine the minimum bactericidal concentration (MBC) with 8 concentrations and 2 controls. Data analysis of the MIC and MBC values was performed using One-Way ANOVA and Kruskal-Wallis parametric tests. The ethanol extract of torch ginger leaves (E. elatior) has minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values against Enterococcus faecalis bacteria. The minimum inhibitory concentration of the ethanol extract of torch ginger leaves is at a concentration of 3.125%, while the minimum bactericidal concentration is at a concentration of 65%.

Hijabi Metal Voice of Baceprot: Discourse on Identity, Gender and Religion in Digital Space / Hijabi Metal Voice of Baceprot: Wacana Identitas, Gender dan Agama di Ruang Digital Pikri, Zainal; Muthmainnah, Inna
Alhadharah: Jurnal Ilmu Dakwah Vol. 23 No. 2 (2024)
Publisher : UIN Antasari Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18592/alhadharah.v23i2.15460

This study examines the intersection of religion, gender, and culture through the case of Voice of Baceprot (VOB), an Indonesian hijab-wearing metal band. Utilizing Kimberlé Crenshaw’s intersectionality theory and Laclau and Mouffe’s discourse analysis, the research analyzes YouTube viewer comments to explore how VOB’s identity as Muslim women metal musicians is constructed and contested. The study employs a qualitative approach, collecting comments from VOB’s most-viewed YouTube videos using Python-based scripts and analyzing them thematically and discursively. This methodology reveals competing narratives: critics perceive the hijab as incompatible with traditional metal aesthetics, while supporters celebrate its role in redefining gender and religious norms in popular music. By navigating these complex discourses, VOB challenges stereotypes within conservative Islamic norms and global metal culture, presenting the hijab as a symbol of personal choice and empowerment. The findings highlight how VOB redefines heavy metal as a global, inclusive genre and demonstrates the power of music to transcend cultural and religious barriers.

Hijabi Metal Voice of Baceprot: Discourse on Identity, Gender and Religion in Digital Space / Hijabi Metal Voice of Baceprot: Wacana Identitas, Gender dan Agama di Ruang Digital Pikri, Zainal; Muthmainnah, Inna
Alhadharah: Jurnal Ilmu Dakwah Vol. 23 No. 2 (2024)
Publisher : UIN Antasari Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18592/alhadharah.v23i2.15460

This study examines the intersection of religion, gender, and culture through the case of Voice of Baceprot (VOB), an Indonesian hijab-wearing metal band. Utilizing Kimberlé Crenshaw’s intersectionality theory and Laclau and Mouffe’s discourse analysis, the research analyzes YouTube viewer comments to explore how VOB’s identity as Muslim women metal musicians is constructed and contested. The study employs a qualitative approach, collecting comments from VOB’s most-viewed YouTube videos using Python-based scripts and analyzing them thematically and discursively. This methodology reveals competing narratives: critics perceive the hijab as incompatible with traditional metal aesthetics, while supporters celebrate its role in redefining gender and religious norms in popular music. By navigating these complex discourses, VOB challenges stereotypes within conservative Islamic norms and global metal culture, presenting the hijab as a symbol of personal choice and empowerment. The findings highlight how VOB redefines heavy metal as a global, inclusive genre and demonstrates the power of music to transcend cultural and religious barriers.

KOMBINASI LATENT SEMANTIC INDEXING DAN SUPPORT VECTOR MACHINE PADA KLASIFIKASI DOKUMEN AKREDITASI: STUDI KASUS : PASCASARJANA UNIVERSITAS NEGERI MEDAN Warjaya, Angga; As, Mansur; Muthmainnah, Inna; Mulyana, Sri; Iskandar Al Idrus, Said; Arnita, Arnita; Taufik, Insan
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 4 (2025): JATI Vol. 9 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i4.14102

Pengelolaan dokumen akreditasi yang efisien menjadi tantangan utama dalam pendidikan tinggi akibat volume dokumen yang besar dan format yang bervariasi. Penelitian ini bertujuan untuk mengembangkan metode klasifikasi otomatis menggunakan kombinasi latent semantic indexing dan support vector machine guna meningkatkan akurasi dan efisiensi pengelolaan dokumen akreditasi. Akurasi dalam penelitian ini mengacu pada ketepatan sistem dalam mengidentifikasi kategori dokumen sesuai kriteria akreditasi, sementara efisiensi mencerminkan percepatan dan penyederhanaan proses klasifikasi dibandingkan dengan metode manual. Dataset terdiri dari 230 dokumen yang dikategorikan berdasarkan kriteria Lembaga Akreditasi Mandiri Kependidikan, dengan 115 dokumen untuk Kriteria 6 (Pendidikan) dan 115 dokumen untuk Kriteria 7 (Penelitian), kemudian dibagi menjadi data latih dan uji dengan rasio 60:40. Proses klasifikasi dilakukan melalui beberapa tahap, termasuk pre-processing teks, ekstraksi fitur semantik, serta optimasi parameter model untuk memperoleh hasil terbaik. Pengujian menunjukkan bahwa metode yang diusulkan mampu mencapai tingkat akurasi sebesar 91%, dengan validasi silang sebesar 94,21%. Hasil ini menunjukkan bahwa pendekatan yang digunakan efektif dalam mengotomatisasi klasifikasi dokumen akreditasi, sehingga dapat mempercepat proses evaluasi serta meningkatkan efisiensi manajemen dokumen dalam institusi pendidikan tinggi.

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