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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) snimed Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Seminar Nasional Informatika (SEMNASIF) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) J-Dinamika: Jurnal Pengabdian Kepada Masyarakat JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal E-Komtek Journal of Informatics, Information System, Software Engineering and Applications (INISTA) JPM: JURNAL PENGABDIAN MASYARAKAT Journal of Dinda : Data Science, Information Technology, and Data Analytics JUSTIN (Jurnal Sistem dan Teknologi Informasi) Prosiding Seminar Nasional Pengabdian Kepada Masyarakat IJCOSIN : Indonesian Journal of Community Service and Innovation DEVOTE: Jurnal Pengabdian Masyarakat Global eProceedings of Engineering Jurnal Abdimas Indonesia Jurnal INFOTEL International Journal of Artificial Intelligence in Medical Issues JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) IJCOSIN : Indonesian Journal of Community Service and Innovation
Siti Khomsah, Siti
Universitas Telkom, Kampus Purwokerto
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Medicine & Pharmacology Public Health Social Sciences Transportation Other

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Analisis Emosi Wisatawan Menggunakan Metode Lexicon Text Analysis Rahmadani, Dea Caesy; Khomsah, Siti; Fathoni, M Yoka
Jurnal Teknik Informatika dan Sistem Informasi Vol 10 No 1 (2024): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v10i1.6690

Abstract

Travelers often write comments on the internet, usually about experiences, opinions, and even complaints. Comment data on the internet can provide information for stakeholders. This information can be extracted using text analysis methods such as positive and negative sentiments. Sentiments can be detailed into eight types of emotions. This study aims to extract emotions from tourists' comments on Google Map, especially on tourist-site accounts in BARLINGMASCAKEB. The dataset comments were crawled from ten tourism objects in BARLINGMASCAKEB. The method used is Lexicon Emotion Analysis. The results show that the majority of tourists have positive experiences. It is shown by the emotion "joy" and "trust." Emotions "joy" and "trust" have positive meanings, so it can be said that the majority of tourists feel positive emotions. There are sites that present highest emotions of "joy": Aquarium-Purbasari-Pancuran-Mas with 33.52%, Lembah-Asri-Serang with 30.85%, Sanggaluri-Reptile-Park by 30, 27%, Baturaden Botanical-Gardens with 27, 67 %, and Curug-Jenggala by 23.4%. At the same time, the highest types of "trust" emotions are Benteng-Pandem with 27.41%, Arjuna-Temple with 26.6%, Sikidang-Crater with 20.71%, and Menganti-Beach with 25, 74%. Only one site, the World Miniature Park, gives the highest "anticipation" emotion. Usually, caring words represent anticipation emotions, so they can still be categorized into positive emotions. The extraction of emotions is affected by the process of emotion-labeling of each comment, so further research is recommended to develop a lexicon emotion dictionary. The results of this study are expected to provide benefits for the development of the tourism industry in the BARLINGMASCAKEB area and for the academic world, especially regarding the application of text mining in the tourism sector
Perbandingan Algoritma XGBOOST Dan LSTM untuk Memprediksi Harga Bitcoin Berdasarkan Harga Harian, Sentimen, dan Google Trends Index Zundina Ulya, Fadilla; Khomsah, Siti; Annisa Ferani Tanjung, Nia
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 6: Desember 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025126

Abstract

Bitcoin merupakan salah satu jenis cryptocurrency yang banyak digunakan karena transaksinya yang aman, cepat, dan berpotensi memberikan keuntungan signifikan. Namun, volatilitas harga yang tinggi membuat aktivitas transaksi berisiko, karena pergerakan harga tidak hanya dipengaruhi oleh faktor internal, tetapi juga faktor eksternal seperti sentimen publik dan Google Trends Index (GTI). Penelitian ini bertujuan membandingkan algoritma XGBoost regression dan LSTM for regression dalam memprediksi harga penutupan bitcoin dengan mengintegrasikan variabel harga harian, sentiment, dan GTI ke dalam model regresi yang disesuaikan dengan karakteristik data penelitian, dimana data yang yang digunakan bersifat non-linear, tidak berdistribusi normal, dan mengandung unsur time series. Berdasarkan hasil pengujian, model XGBoost regression terbaik diperoleh pada skenario dengan variabel eksternal. Namun menghasilkan nilai RMSE sebesar 5169,898 USD dan R2-Score sebesar -13%, yang menunjukkan adanya overfitting dan model kurang tepat untuk data time series. Sebaliknya, model LSTM for regression dengan variabel eksternal dan kombinasi hyperparameter terbaik menunjukkan performa yang lebih unggul dengan RMSE sebesar 1378,55 USD dan R2-Score sebesar 92%. Model ini tidak menunjukkan indikasi overfitting dan mampu mereplikasi pola pergerakan harga secara akurat. Hal ini menunjukkan bahwa LSTM for regression lebih mampu mengenali pola temporal dalam data historis. Selain itu, fitur harga historis, khususnya Open teridentifikasi sebagai variabel paling dominan berdasarkan hasil analisis menggunakan metode SHAP.   Abstract Bitcoin is one type of cryptocurrency that is widely used because its transactions are safe, fast, and have the potential to provide significant profits. However, high price volatility makes transaction activities risky, because price movements are not only influenced by internal factors, but also external factors such as public sentiment and the Google Trends Index (GTI). This study aims to compare the XGBoost regression and LSTM for regression algorithms in predicting bitcoin closing prices by integrating daily price, sentiment, and GTI variables into a regression model that is adjusted to the characteristics of the research data, where the data used is non-linear, not normally distributed, and contains time series elements. Based on the test results, the best XGBoost regression model was obtained in the scenario with external variables. However, it produces an RMSE value of 5169.898 USD and an R2-Score of -13%, which indicates overfitting and the model is less appropriate for time series data. In contrast, the LSTM for regression model with external variables and the best combination of hyperparameters shows superior performance with an RMSE of 1378.55 USD and an R2-Score of 92%. This model does not show any indication of overfitting and is able to replicate price movement patterns accurately. This shows that LSTM for regression is better able to recognize temporal patterns in historical data. In addition, historical price features, especially Open, are identified as the most dominant variables based on the results of the analysis using the SHAP method.
Pelatihan Dan Pendampingan Perajin Bambu Desa Grujugan Untuk Meningkatkan Kualitas Irat Dan Diversifikasi Produk Siti Khomsah; Novanda Alim Setya Nugraha; Wenny Marlini; Halim Qista Karima; Blandina Hendrawardani
JPM: Jurnal Pengabdian Masyarakat Vol. 4 No. 1 (2023): July 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jpm.v4i1.1042

Abstract

Grujugan Village in Kebumen Regency is bamboo craftsmen village. Eighty percent of the population are bamboo craftsmen. The problems faced are the quality of Irat and product diversification. Irat is a thin sheet of bamboo, it is the raw material to make a craft. The thickness of the Irat is not at the same standard because the craftsman thinning material using traditional knife. In other hand, capacity and quickness of production is low because craftsmen do it manually. Meanwhile, product variations are still in a few kinds, such as bamboo’s hat (tudung), several type of food container, and hand-held-fan. Therefore, this community service aims to provide workshops and mentoring on the diversification of existing crafts and manufacture of better bamboo materials using machines. To do this, the community service team will be training how to making cups from bamboo and also how to thinning using the bamboo’s slicing machines. These activities took place form Juli until October in Grujugan Village. Based on the effort, The quality and thickness of the fibers is getting better and uniform. The use of machines can increase the capacity and rate production of Irat, from 1280 to15360 pieces per 8 hours. And also, craftsmen already have the capability to make new products, namely bamboo cup, although the quality is not yet fit for standard market.
Semi-Supervised Sentiment Classification Using Self-Learning and Enhanced Co-Training Agus Sasmito Aribowo; Siti Khomsah; Shoffan Saifullah
JURNAL INFOTEL Vol 17 No 3 (2025): August
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i3.1344

Abstract

Sentiment classification is usually done manually by humans. Manual senti- ment labeling is ineffective. Therefore, automated labeling using machine learning is es- sential. Building a computerized labeling model presents challenges when labeled data is scarce, which can decrease model accuracy. This study proposes a semi-supervised learn- ing (SSL) framework for sentiment analysis with limited labeled data. The framework integrates self-learning and enhanced co-training. The co-training model combines three machine learning methods: Support Vector Machine (SVM), Random Forest (RF), and Lo- gistic Regression (LR). We use TF-IDF and FastText for feature extraction. The co-training model will generate pseudo-labels. Then, the pseudo-labels from models (SVM, RF, LR) are checked to choose the highest confidence — this is called self-learning. This framework is applied to English and Indonesian language datasets. We ran each dataset five times. The performance difference between the baseline model (without pseudo-labels) and SSL (with pseudo-labels) is not significant; the Wilcoxon Signed-Rank Test confirms it, obtaining a p- value < 0.05. Results show that SSL produces pseudo-labels on unlabeled data with quality close to the original labels on unlabeled data. Although the significance test performs well on four datasets, it has not yet surpassed the performance of the supervised classification (baseline). Labeling using SSL proves more efficient than manual labeling, as evidenced by the processing time of around 10-20 minutes to label thousands to tens of thousands of samples. In conclusion, self-learning in SSL with co-training can effectively label unla- beled data in multilingual and limited datasets, but it has not yet converged across various datasets.
Peningkatan Kualitas Produk dan Kapasitas Pemasaran Kelompok Wanita Tani Migunani Dusun Druwo Yogyakarta Siti Khomsah; Atika Ratnadewi; Aina Latifa Riyana Putri; Irwan Susanto; Rizal Wahyu Pratama; Mikhael Setia Budi; Khulika Malkan
Indonesian Journal of Community Service and Innovation Vol. 5 No. 3 (2025): Desember 2025
Publisher : LPPM IT Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/ijcosin.v5i3.10113

Abstract

Kelompok Wanita Tani (KWT) Migunani di Dusun Druwo, Kapanewon Sewon, Kabupaten Bantu, Yogyakarta bergerak di bidang pertanian dan usaha makanan seperti keripik dan kue basah. Meski cita rasa produk sudah baik, kualitas kemasan, mutu produk, dan pengelolaan keuangan masih kurang bagus. Produk dikemas dengan plastik sederhana tanpa deskripsi produk lengkap, serta pemasaran secara tradisional melalui bazar dan pasar desa. Lebih jauh, mereka tidak mencatat biaya dan pendapatan bisnisnya, sehingga mengakibatkan ketidakmampuan untuk menentukan laba rugi. Pengabdian masyarakat ini bertujuan memberikan solusi atas permasalahan tersebut dengan fokus pada tiga aspek: perbaikan kemasan, pemanfaatan digital marketing, dan pengelolaan keuangan usaha kecil. Metode pelaksanaan mencakup pelatihan dan pendampingan, yaitu: (1) pembuatan kemasan menarik termasuk desain logo dan label produk; (2) digital marketing melalui Instagram dan WhatsApp Business; (3) keuangan UMKM, seperti penggunaan QRIS, pencatatan arus kas, buku kas sederhana, perhitungan HPP dan BEP; serta (4) pendampingan sertifikasi halal. Hasil kegiatan menunjukkan peningkatan branding produk, dengan adanya logo, label halal, dan informasi produk lengkap. Anggota KWT juga mulai mampu mengelola keuangan secara lebih terstruktur dan memanfaatkan teknologi digital untuk pemasaran dan transaksi. Program ini diharapkan mampu meningkatkan daya saing produk KWT Migunani sekaligus memperkuat kemandirian ekonomi anggotanya.
Co-Authors Aditya Dwi Putro Aditya Dwi Putro Wicaksono Agus Sasmito Aribowo Agus Sasmito Aribowo Agus Sasmito Aribowo Agus Sasmito Aribowo Agus Sasmito Aribowo Aina Latifa Riyana Putri Alam Patria Utama Andriyani, Widyastuti Annisa Ferani Tanjung, Nia Ardian, Miko Atika Ratnadewi Ayuningtyas, Puji Bahtiar, Arvinanto Bambang Purnomosidi Dwi Putranto Blandina Hendrawardani Boma Bayu Baskoro Claudia Larasvaty Dea Caesy Rahmadani Dian Nurdiansyah Edi Faizal Edi Winarko Halizah Basiron Hutomo, Muhammad Ihsan Prawira Ibrahim, Fajar Irfani, Muhammad Irwan Susanto Karima, Halim Qista Khulika Malkan Lutfi Hakim, Ahmad M Yoka Fathoni Mikhael Setia Budi Mufidah, Lulu Nenen Isnaeni Noor Fazilla Abd Yusof Novanda Alim Setya Nugraha Novian Adi Prasetyo Nur Fauzi, Nadea Putri Nur, Yohani Setya Rafika Putra Wicaksono, Aditya Dwi Rahmadani, Dea Caesy Ramadhani, Rima Dias Ridwan Pandiya Rizal Wahyu Pratama Rona Nisa SA Saputri, Rezi Iwardani Sausan Sena Wijaya Sena Wijayanto Shoffan Saifullah Sudianto Syamkalla, Mohammad Teddy Tri Ginanjar Laksana Ummi Athiyah Utami, Annisaa Wenny Marlini Widiyanto, Agung Zundina Ulya, Fadilla