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All Journal JSM (Jurnal SIFO Mikroskil) Jurnal Ilmu Komputer dan Agri-Informatika Channing: Journal of English Language Education and Literature Jurnal Bidang Pendidikan Dasar Indonesian Journal of Science and Mathematics Education Jurnal SOLMA JMM (Jurnal Masyarakat Mandiri) JUTIM (Jurnal Teknik Informatika Musirawas) IJISTECH (International Journal Of Information System & Technology) Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal JURNAL PENDIDIKAN MIPA JSR : Jaringan Sistem Informasi Robotik Innovation in Research of Informatics (INNOVATICS) IJISTECH Jurnal Indonesia Mengabdi Sinar Sang Surya: Jurnal Pusat Pengabdian kepada Masyarakat JoMMiT : Jurnal Multi Media dan IT Jurnal Pengabdian Masyarakat (ABDIRA) Jurnal Minfo Polgan (JMP) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Jurnal Sains Informatika Terapan (JSIT) Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII Instink: Inovasi Pendidikan, Teknologi Informasi dan Komputer PROSIDING SEMINAR NASIONAL PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT (SNPPM) UNIVERSITAS MUHAMMADIYAH METRO Jurnal Informatika: Jurnal Pengembangan IT Jurnal abimana (Jurnal Pengabdian Kepada Masyarakat Nasional) Jurnal Indonesia Mengabdi Genta Mulia
Amaliah, Khusnatul
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Pembangunan Sistem Informasi Praktik Pengalaman Lapangan Pendidikan Berbasis Web Rizki, Uli; Amaliah, Khusnatul; Murtiningseh, Murtiningseh
Instink: Inovasi Pendidikan, Teknologi Informasi dan Komputer Vol. 1 No. 1 (2022): Edisi April
Publisher : Prodi Pendidikan Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30599/instink.v1i1.1501

Abstract

Mahasiswa Fakultas Ilmu Pendidikan Universitas Nurul Huda wajib untuk mengikuti Praktik Pengalaman Lapangan Kependidikan (PPLK) agar memiliki keterampilan riil menjadi seorang pengajar. Saat ini belum terdapat sistem informasi untuk mendukung jalannya kegiatan PPL mulai dari pendaftaran, pemilihan sekolah, pemilihan guru pendamping, dan laporan kegiatan. Proses manual menjadi kurang efektif dalam menunjang tahapan kegiatan PPL. Penelitian ini bertujuan untuk membuat sistem informasi yang bisa membantu proses pengurusan kegiatan sesuai dengan alur di lingkungan Fakultas Ilmu Pendidikan Universitas Nurul Huda. Sistem ini memiliki fitur kuota mahasiswa setiap sekolah, pemilihan guru pamong, penilaian laporan mingguan dan laporan akhir mahasiswa oleh dosen pendamping lapangan. Metode pengembangan sistem yang digunakan adalah exreme programming. Kata kunci—PPL, Sistem Informasi, Pendidikan
Upaya Peningkatan Kemampuan Siswa dalam Menggunakan Aplikasi Myob Accounting di SMK Negeri 1 Belitang Madang Raya Rohmah, Miftakhur; Dewi, Tri Ratna; Amaliah, Khusnatul; Sutati, Sutati
Jurnal Indonesia Mengabdi Vol. 1 No. 2 (2019): December Edition
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LPPM) STKIP Nurul Huda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30599/jimi.v1i2.594

Abstract

Computer accounting is one of the subjects that is an important part of the connection with the industrial revolution 4.0. The development of this era requires teachers and students to be able to carry out learning processes that is oriented to technology that is adapted to the industrial world environment as a target of students' self-development. The perceived benefit of understanding MYOB Accounting is that we can automate bookkeeping in a complete, detailed, and accurate manner. MYOB is easy to understand and learn. Likewise with the functions it has, the explanation is very clear. The field of accounting activities in the current era is developing in the process and implementation. The importance of the skills is possessed by students in preparing for the Competency Examination and preparing for the world of work, this is a reason for the need for appropriate and appropriate learning to hone the abilities of students.
Pelatihan Bahasa Inggris Dalam Pemasaran Online Guna Membangun Jiwa Wirausaha Perempuan Kuspiyah, Hastuti Retno; Amaliah, Khusnatul; Kurniati, Nia; Marsa, Shela Septina; Ariansyah, Rio Tegar
Jurnal Indonesia Mengabdi Vol. 3 No. 1 (2021): June Edition
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LPPM) STKIP Nurul Huda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30599/jimi.v3i1.1621

Abstract

English training in online marketing to build the entrepreneurial spirit of women during the Covid-19 Pandemic, PRIMA Village was carried out as a form of solution related to problems due to the impact of the world economy. The training activities took place at the village hall with the target of PRIMA women at Tumijaya village. Based on the results,, it was found that this activity received a positive response from the village and the participants. It is an effort to facilitate learning to better manage businesses, gain economic benefits from their activities and learn from one another. With the English language training activities for PRIMA women in Tumijaya village, it is a form of concern to help them find their potential and provide knowledge for readiness to build an entrepreneurial spirit as well as an effort to realize the ideals of entrepreneurship in order to realize gender equality and justice during the Covid-19 pandemic.
Peningkatan Daya Saing UMKM Binaan Polinela melalui Pemanfaatan Teknologi Artificial Intelligence (AI) Amaliah, Khusnatul; Eko Win Kenali; Dani Rofianto; Tiara Kurnia Khoerunnisa; Jaka Fitra; Halim Fathoni; Fitri, Melisa
Jurnal Indonesia Mengabdi Vol. 6 No. 2 (2024): December Edition
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LPPM) STKIP Nurul Huda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30599/jimi.v6i2.3857

Abstract

Pendampingan penggunaan teknologi Artificial Intelligence (AI) dan digitalisasi bagi Usaha Mikro, Kecil, dan Menengah (UMKM) di Indonesia menjadi sangat penting dalam meningkatkan daya saing dan keberlanjutan bisnis. Pengabdian ini bertujuan untuk menganalisis dampak pendampingan yang diberikan kepada UMKM, khususnya dalam konteks peningkatan kualitas produk, pemasaran, dan pemahaman teknologi. Pemanfaatan Teknologi Artificial Intelligence ini fokus pada pemanfaatan teknologi AI dalam pembuatan konten promosi yang efektif. Pengabdian ini dilakukan dengan pendekatan metode Participatory Action Research dimana pengabdian berorientasi pada pada pemberdayaan dalam menyelesaikan permasalahan mitra. Hasil kegiatan menunjukkan bahwa pendampingan ini berhasil meningkatkan kualitas kemasan produk, visibilitas di pasar, dan pemahaman teknologi di kalangan mitra UMKM. Temuan ini menegaskan bahwa digitalisasi dan penggunaan AI tidak hanya membantu UMKM dalam menghadapi tantangan pasar, tetapi juga mendorong inovasi dan pertumbuhan yang berkelanjutan.
Performance Evaluation Of SVM With Parameter Optimization On Credit Card Fraud Data Subset Using SMOTE Mahardika, Ahmad Farrel; Fahrezi, Irza Nuzul; Alleredha, Muhammad Hadya; Amaliah, Khusnatul; Rofianto, Dani
IJISTECH (International Journal of Information System and Technology) Vol 9, No 1 (2025): The June Edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v9i1.398

Abstract

This study evaluates the performance of the Support Vector Machine (SVM) algorithm in detecting credit card fraud by overcoming the class imbalance problem using the Synthetic Minority Oversampling Technique (SMOTE) technique and parameter optimization through Grid Search. The dataset used is sourced from Kaggle, consists of 10,001 transactions, and has been balanced. SMOTE is applied exclusively to the training data to prevent data leakage. The optimization process produces the best parameters at a value of C = 10 and gamma = 0.1. Model evaluation is carried out using recall, precision, F1-score, and AUC-ROC metrics. The results show a significant increase in performance in recognizing fraudulent transactions. The final model recorded a recall of 0.68, precision 0.90, F1-score 0.77, and AUC-ROC 0.98. These findings prove that the combination of SMOTE techniques and parameter optimization can improve the effectiveness of SVM in classifying minority classes more accurately. This approach is considered to have great potential to be applied in automated fraud detection systems in the financial sector.
Performance Evaluation Of SVM With Parameter Optimization On Credit Card Fraud Data Subset Using SMOTE Mahardika, Ahmad Farrel; Fahrezi, Irza Nuzul; Alleredha, Muhammad Hadya; Amaliah, Khusnatul; Rofianto, Dani
IJISTECH (International Journal of Information System and Technology) Vol 9, No 1 (2025): The June Edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v9i1.398

Abstract

This study evaluates the performance of the Support Vector Machine (SVM) algorithm in detecting credit card fraud by overcoming the class imbalance problem using the Synthetic Minority Oversampling Technique (SMOTE) technique and parameter optimization through Grid Search. The dataset used is sourced from Kaggle, consists of 10,001 transactions, and has been balanced. SMOTE is applied exclusively to the training data to prevent data leakage. The optimization process produces the best parameters at a value of C = 10 and gamma = 0.1. Model evaluation is carried out using recall, precision, F1-score, and AUC-ROC metrics. The results show a significant increase in performance in recognizing fraudulent transactions. The final model recorded a recall of 0.68, precision 0.90, F1-score 0.77, and AUC-ROC 0.98. These findings prove that the combination of SMOTE techniques and parameter optimization can improve the effectiveness of SVM in classifying minority classes more accurately. This approach is considered to have great potential to be applied in automated fraud detection systems in the financial sector.
Ethnoscience-Based Interactive E-Module: E-Module Development on Nonrenewable Resources Topic Widayanti, Widayanti; Amaliah, Khusnatul; Sholikhah, Ainun Ummi; Kurniawan, Andrew
Indonesian Journal of Science and Mathematics Education Vol. 5 No. 3 (2022): Indonesian Journal of Science and Mathematics Education
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijsme.v5i3.13806

Abstract

This study aims to develop an ethnoscience-based interactive e-module with the topic of non-renewable resources. Research and Development in this study adopted the ADDIE theory by Dick and Carry which consists of five stages: 1) Analysis, 2) design, 3) development, 4) implementation, and 5) evaluation. This research only reached the third stage because at that stage the research objectives had been achieved. The instrument of this research is a Likert scale questionnaire which is analyzed by percentage. The results showed that the interactive e-module was declared feasible to be applied in learning with content and media expert validators giving an average score of above 75%. Student responses to this e-module also reached more than 75%, so this interactive e-module was stated to be very interesting. The conclusion of this study is that this interactive e-module is valid so that it can be applied in physics learning. For further research, it is hoped to develop e-modules on different topics.
Penerapan Algoritma Random Forest dalam Prediksi Emosi Musik Berdasarkan Karakteristik Fitur Audio Spotify Marsya, Nabila Defany; Munadhil, Muhammad Mufrih; Dzakyananta, M Alvin; Amaliah, Khusnatul; Rofianto, Dani
Jurnal Sains Informatika Terapan Vol. 4 No. 2 (2025): Jurnal Sains Informatika Terapan (Juni, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i2.648

Abstract

Emotion classification in music is a crucial aspect in developing context-aware recommendation systems that respond to the listener’s mood. The Random Forest algorithm is used to map song emotions based on Spotify audio features, namely valence, energy, loudness, and danceability, which reflect the psychological and acoustic aspects of music. The dataset was collected through the Spotify Web API and public repositories, consisting of songs released between 2009 and 2019. Data processing involved normalization and labeling emotions into five categories: Angry, Calm, Happy, Neutral, and Sad. The model was trained using 70% of the data and tested with the remaining 30%. Evaluation results showed an accuracy of 98.75%, with perfect F1-scores for the Happy and Sad categories. Valence and energy were found to be the most influential features, while Calm was often confused with Neutral due to similar acoustic patterns. These findings demonstrate that the Random Forest approach is effective in accurately and consistently classifying music emotions based on audio features.
Model Ensembel untuk Deteksi Depresi di Twitter Berbahasa Indonesia Fitri, Melisa; Nurkhotimah, Jihan Susan; Ihsan, Faiz Nurfaadhil; Amaliah, Khusnatul; Rofianto, Dani
Jurnal Sifo Mikroskil Vol. 26 No. 2 (2025): JSM VOLUME 26 NOMOR 2 TAHUN 2025
Publisher : Fakultas Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55601/jsm.v26i2.1812

Abstract

Pentingnya Deteksi dini gangguan kesehatan mental khususnya depresi di era digital saat ini di mana individu lebih cenderung mengekspresikan kondisi emosionalnya melalui media sosial. Penelitian ini bertujuan untuk mengembangkan model ensembel Machine Learning dalam mendeteksi gejala depresi pada postingan media sosial berbahasa Indonesia, khususnya dari platform Twitter. Dataset yang digunakan adalah Depression and Anxiety in Twitter (ID) yang terdiri dari 6.980 teks berlabel. Proses preprocessing mencakup pembersihan data, vektorisasi dengan TF-IDF, dan pemisahan data menggunakan metode overfitting. Empat algoritma yaitu Support Vector Machine, Naïve Bayes, Random Forest, dan AdaBoost dikombinasikan menggunakan Voting Classifier dengan pendekatan soft voting. Evaluasi model dilakukan menggunakan metrik akurasi, precision, recall, dan F1-score, serta visualisasi heatmap korelasi dan learning curve. Hasil menunjukkan bahwa model Voting Classifier menghasilkan kinerja terbaik dengan F1-score makro sebesar 0,996, menunjukkan bahwa pendekatan ensembel efektif dalam meningkatkan akurasi dan stabilitas klasifikasi. Penelitian ini berkontribusi dalam pengembangan sistem deteksi dini gangguan mental berbasis teks bahasa Indonesia yang dapat digunakan oleh lembaga kesehatan, institusi pendidikan, dan organisasi sosial.
Metode Ekstraksi Fitur dan Klasifikasi Visual Untuk Identifikasi Kualitas Pangan Lokal Berbasis Citra Digital Rofianto, Dani; Amaliah, Khusnatul; Khoerunissa, Tiara Kurnia; Fitri, Melisa
Jurnal Ilmu Komputer dan Agri-Informatika Vol. 12 No. 2 (2025)
Publisher : Sekolah Sains Data, Matematika, dan Informatika. Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.12.2.178-188

Abstract

Mutu pangan lokal memiliki peran penting dalam menjaga ketahanan pangan dan meningkatkan daya saing produk di pasar. Namun, penentuan mutu masih mengandalkan inspeksi visual manual yang bersifat subjektif dan tidak konsisten, sehingga berpotensi menimbulkan kerugian pascapanen serta menurunkan kepercayaan konsumen. Penelitian ini bertujuan untuk mengembangkan sistem klasifikasi mutu pangan berbasis citra digital dengan memanfaatkan arsitektur deep learning EfficientNetV2B0 melalui pendekatan transfer learning. Dataset yang digunakan mencakup delapan kelas dari empat komoditas utama, yaitu apel, pisang, tomat, dan pare dalam kondisi segar dan tidak segar. Seluruh citra diproses melalui tahap prapengolahan berupa pengubahan ukuran dan normalisasi, serta dilakukan augmentasi data untuk meningkatkan variasi dan mencegah overfitting. Model dilatih dengan konfigurasi ringan dan dievaluasi menggunakan akurasi, presisi, recall, F1-score, serta confusion matrix. Hasil penelitian menunjukkan bahwa model mampu mencapai akurasi keseluruhan sebesar 99.8 persen dengan nilai presisi, recall, dan F1-score mendekati sempurna pada hampir semua kelas. Temuan ini membuktikan bahwa penerapan transfer learning dan augmentasi data efektif dalam membedakan pangan segar dan tidak segar berdasarkan citra digital. Secara praktis, sistem ini berpotensi mendukung petani dan pelaku usaha kecil menengah dalam melakukan inspeksi mutu yang lebih objektif, efisien, dan konsisten.
Co-Authors Ainun Ummi Sholikhah Alleredha, Muhammad Hadya Andrew Kurniawan Andri Febriyadi Putra Andri Febriyadi Putra Anggar Anugrah Satrya Wiratama Aprilita Ekasari Ariansyah, Rio Tegar Ariyansyah Dani Rofianto Dimas Ramadhani Dzakyananta, M Alvin Eka Setya Ningsih Eko Win Kenali Fahrezi, Irza Nuzul Fathurrahman Kurniawan Ikhsan Fitra, Jaka Fitri, Melisa Fitriani Halim Fathoni Halim Fathoni Hastuti Retno Kuspiyah, Hastuti Retno Hevia Purnama sari Hijriani, Astria Ihsan, Faiz Nurfaadhil Ivylentine Datu Palittin Jua, Selestina Kostaria K Ikshan, Fathurrahman Kafka Armelsya Ketut Putri Ketut Putri Khoerunissa, Tiara Kurnia Kurnia Khoerunnisa, Tiara Kurniawan, Andrew M Iqbal Mustofa M. Frans Diantoro M. Frans Diantoro M. Ilham Abdillah Mahardika, Ahmad Farrel Marsa, Shela Septina Marsya, Nabila Defany Masrifah, Ulfa Melisa Fitri Merta Simbolon Miftakhur Rohmah, Miftakhur Mochammad Arfian Ardiansyah Muhammad Nurcahyo Sasongko Muhammad Rakha Ramadhan Munadhil, Muhammad Mufrih Murtiningseh Murtiningseh Murtiningseh, Murtiningseh Nesi Anti Andini Ni Wayan Sevilla Sekarsari Nia Kurniati Nia Kurniati Nurbani Kalsum Nurkhotimah, Jihan Susan Oki Arifin Oki Arifin Rio Tegar Ariansyah Rohmah, Miftakhur Safitri, Egi Sahrul Alan Setiawan Sahrul Alan Setiawan Sheila Oki Tarina Shela Septina Marsa Sholikhah, Ainun Ummi Somia Ela Safrika Sri Enggar Kencana Dewi Surfiana Surfiana Susanti Susanti Sutati Sutati Sutati, Sutati Tanto Pratondo Utomo Tri Ratna Dewi Tutik Yuliatun Ulfa Masrifah Vina Lupitasari Vina Lupitasari Widayanti Widayanti Widayanti Widayanti WINARTO Yusep Windhu Ari Wibowo Yusep Windhu Ari Wibowo