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All Journal Natural Science: Journal of Science and Technology Creative Information Technology Journal Jurnal Sains dan Informatika ILKOMNIKA: Journal of Computer Science and Applied Informatics JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Jurnal Ilmiah Intech : Information Technology Journal of UMUS Jurnal Telkommil Indo-MathEdu Intellectuals Journal Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Journal Automation Computer Information System (JACIS) Informatics, Electrical and Electronics Engineering Absorbent Mind Education and Counseling Journal TRANSPUBLIKA INTERNATIONAL RESEARCH IN EXACT SCIENCES (TIRES) Tematik: Jurnal Penelitian Pendidikan Dasar JURNAL EDUAKSI (Jurnal Edukasi dan Penelitian Tindakan Kelas) Welfare: Jurnal Pengabdian Masyarakat
Harliana Harliana
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Religion Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Nursing Physics Public Health Social Sciences Other

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Pengelolaan Limbah Rumah Tangga Berbasis Komunitas untuk Produksi Pupuk Kompos Organik Alam, Yuniar; Harliana, Harliana; Haryuni, Nining; Oktaviani, Ragil Tri
Welfare : Jurnal Pengabdian Masyarakat Vol. 2 No. 4 (2024): Welfare : December 2024
Publisher : Fakultas Ekonomi dan Bisnis Islam, IAIN Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30762/welfare.v2i4.1964

Abstract

Household waste, which is mostly organic waste, such as leftover food, vegetables, fruit peels, and food processing waste, has the potential to pollute the environment if not managed properly. This community service aims to improve the knowledge and skills of the people of Sumberkepuh Village, Wonosari Hamlet, in utilizing household waste into organic compost. This program aims to reduce odor pollution and methane gas emissions and promote environmentally friendly waste management. The partners of this activity are local housewives and farmers. The method used is Participatory Action Research (PAR), which involves delivering materials, interactive discussions, demonstrations of organic waste processing, and question and answer sessions. The results of the activity showed an increase in the community's understanding and skills in processing organic waste into compost. This program received a positive response from participants, who are now more motivated to manage household waste independently and sustainably.
ANALISIS AKURASI NAÏVE BAYES DAN KNN DALAM PENENTUAN PENERIMA PKH DI LOMBOK UTARA Nuraeni, Septiya; Harliana, Harliana; Prabowo, Tito
Journal of Information System Management (JOISM) Vol. 5 No. 2 (2024): Januari
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/joism.2024v5i2.1205

Abstract

Saat ini Kemiskinan merupakan permasalahan utama di negara berkembang karena berhubungan dengan taraf hidup masyarakat yang rendah. Sebagai salah satu upaya penanggulangan kemiskinan pemerintah mengeluarkan bantuan yang diberi nama PKH, dimana bantuan ini hanya diperuntukkan bagi Rumah Tangga Sangat Miskin (RTSM) melalui beberapa ketentuan yang diberlakukan. Beberapa penelitian mengenai PKH sudah banyak dilakukan sebelumnya baik menggunakan algoritma naïve bayes, KNN, C4.5, decision tree, optimasi naïve bayes dengan smote, Gradient Boosted Trees, simple additive weight (SAW), ID3, AHP dan sebagainya. Namun penelitian ini hanya akan membandingkan dan menganalisis akurasi yang dihasilkan oleh Naïve Bayes Classification dan KNN guna menentukan penerima PKH di Lombok Utara. Atribut yang digunakan dalam analisis ini meliputi keluarga prasejahtera, ibu hamil, pendidikan, umur dan disabilitas. Berdasarkan hasil penelitian diperoleh kesimpulan bahwa tingkat akurasi yang dihasilkan oleh Naïve Bayes Classification memiliki nilai yang lebih tinggi jika dibandingkan dengan KNN dengan nilai rata – rata 77% melalui 3 skenario pengujian. Sedangkan pada pengujian recall, performa KNN lebih baik jika dibandingkan dengan Naïve Bayes yaitu 100% pada pengujian pertama dan 75% pada pengujian kedua.
STUDI LITERATUR MENGEMBANGKAN KEMAMPUAN BERBAHASA ANAK USIA DINI MELALUI KEGIATAN BERCERITA Harliana, Harliana; Bey Alfina, Azria; Hermanto, Hermanto
Consilium: Education and Counseling Journal Vol 6 No 1 (2026): Edisi September- Maret
Publisher : Biro 3 Kemahasiswaan dan Kerjasama Universitas Abduracman Saleh Situbondo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36841/consilium.v6i1.7176

Abstract

This study aims to develop early childhood language skills through storytelling methods. Early childhood is in its golden age where their development is very rapid and sensitive to various stimuli. Language as a means of communication plays an important role in child development. This study used literature study and interview methods to collect data. Data analysis is carried out with the Miles and Huberman model which includes data reduction, data presentation, and conclusion/verification. The findings showed that the use of media such as picture storybooks, hand puppets, calendar big books, series drawings, finger puppets, and flannel boards can significantly improve children's language skills. These media not only make storytelling activities more interesting but also support the development of receptive and expressive aspects of children's language. The conclusion of this study is that storytelling methods with the support of the right media can be an effective way to develop early childhood language skills.
Studi Deskriptif Membaca tanpa Mengeja untuk Menstimulasi Kemampuan Literasi Anak Usia 5-6 Tahun: Descriptive Study of Reading Without Spelling to Stimulate Literacy Skill of Children Aged 5—6 Years Harliana, Harliana
Absorbent Mind Vol. 3 No. 1 (2023): Psychology and Child Development
Publisher : Institut Agama Islam Sunan Giri Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37680/absorbent_mind.v3i1.2665

Abstract

This study was conducted with the aim of describing reading activities without spelling to stimulate the literacy ability of children aged 5-6 years in TK Darma Wanita 20 Kembiritan. The study used a qualitative descriptive model. Data collection using observation techniques, interviews, and documentation studies. Data were analyzed during the study (on going process). From the results of the study, data were obtained that early literacy skills (reading) for early childhood using the method of reading without spelling are more efficient than conventional methods (spelling). The reading without spelling method helps students be able to read more complicated sentences within 6 months. This of course must be adjusted to the abilities of each student. So, it can be concluded that strategies in stimulating early childhood literacy skills can be successful if the methods used are in accordance with the characteristics of early childhood learning.
A Comparative Study of Extractive and Generative Approaches for Indonesian Meeting Minutes Summarization Harliana, Harliana; Sismoro, Heri
ILKOMNIKA Vol 7 No 3 (2025): Volume 7, Number 3, December 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v7i3.846

Abstract

This study compares extractive and generative approaches for automatic summarization of Indonesian meeting minutes. Our main scientific contribution is an empirical claim that, under strict zero-shot conditions and without domain adaptation, simple extractive baselines are more reliable than off-the-shelf generative models in preserving both decision content and meeting-context cues (actors/roles). We evaluate three extractive baselines (Lead-3, Random-Extract, TextRank-Simple) against an Indonesian GPT-2 model tested under multiple decoding configurations and an mT5 sequence-to-sequence model in a zero-shot setting. Experiments utilize 30 manually curated meeting minutes. The dataset size is intentionally limited because meeting minutes are heterogeneous and require carefully constructed reference summaries to ensure evaluation validity; the study is positioned as a controlled diagnostic comparison rather than a training or adaptation effort. Performance is measured using ROUGE-1/2/L, summary–to–reference length ratios, simple audits of gender and professional role mentions, correlations between decoding parameters and ROUGE, and paired t-tests. Results show that extractive methods achieve higher and more stable ROUGE scores than zero-shot generative models. TextRank-Simple and Random-Extract perform best, while all GPT-2 configurations remain substantially lower, and mT5 zero-shot fails to align with references. Decoding parameters exhibit only weak correlations with generative performance, and paired t-tests confirm statistically significant differences (p < 0.05). Overall, extractive approaches remain the most dependable choice without in-domain fine-tuning, while generative models are more suitable with adaptation or hybrid strategies.This study compares extractive and generative approaches for automatic summarization of Indonesian meeting minutes. Our main scientific contribution is an empirical claim that, under strict zero-shot conditions and without domain adaptation, simple extractive baselines are more reliable than off-the-shelf generative models in preserving both decision content and meeting-context cues (actors/roles). We evaluate three extractive baselines (Lead-3, Random-Extract, TextRank-Simple) against an Indonesian GPT-2 model tested under multiple decoding configurations and an mT5 sequence-to-sequence model in a zero-shot setting. Experiments utilize 30 manually curated meeting minutes. The dataset size is intentionally limited because meeting minutes are heterogeneous and require carefully constructed reference summaries to ensure evaluation validity; the study is positioned as a controlled diagnostic comparison rather than a training or adaptation effort. Performance is measured using ROUGE-1/2/L, summary–to–reference length ratios, simple audits of gender and professional role mentions, correlations between decoding parameters and ROUGE, and paired t-tests. Results show that extractive methods achieve higher and more stable ROUGE scores than zero-shot generative models. TextRank-Simple and Random-Extract perform best, while all GPT-2 configurations remain substantially lower, and mT5 zero-shot fails to align with references. Decoding parameters exhibit only weak correlations with generative performance, and paired t-tests confirm statistically significant differences (p < 0.05). Overall, extractive approaches remain the most dependable choice without in-domain fine-tuning, while generative models are more suitable with adaptation or hybrid strategies.
Co-Authors Abd. Charis Fauzan Abdul Malik Agustin, Afivatu Pratama Agustina, Nur Afnis Ali Djamhuri Amelia Nur Fitriana Apridin, Apridin Ardiansah, Della K. Bey Alfina, Azria Darmawan, Yudhi Farid Mubarok Handaru Handaru Hermanto Hermanto Hermanto Hermanto Hoszaimah Hoszaimah I Nengah Suwastika Ijah Hadijah Isa Mahfudi Istifadah, Shovi Yatul Karomah, Siti Kosim Kosim Naja Maskuri, Muhammad Nining Haryuni Novi Ismiasih Nuraeni, Septiya Oktaviani, Ragil Tri Prabowo, Tito Raden Mohamad Herdian Bhakti Rosyidah, Yuni Rahmawati Sismoro, Heri Sukerti, Kadek Suliyatin, Suliyatin Wahyudi Wahyudi Weaniati Weaniati Wisni, Firda Wiwi Widayani, Wiwi Yuniar Alam, Yuniar Yusron, RDR Zahro, Ayu Fatimatus Zainurrokhim, Mohammad