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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) 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 International Journal of Artificial Intelligence in Medical Issues JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Siti Khomsah, Siti
Insitut Teknologi Telkom Purwokerto
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Penguatan Branding Desa Wisata Adiluhur Melalui Perancangan Event Desa Wisata yang Adaptif dan Inovatif Novanda Alim Setya Nugraha; Siti Khomsah; Rima Dias Ramadhani
jurnal ABDIMAS Indonesia Vol. 1 No. 4 (2023): Desember : Jurnal ABDIMAS Indonesia
Publisher : STIKes Ibnu Sina Ajibarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59841/jurai.v1i4.678

Abstract

Desa Wisata Adiluhur memiliki potensi wisata yang beragam dan mendapatkan berbagai penghargaan karena pengelolaan desa wisatanya yang maju. Sayangnya, popularitas Deswita Adiluhur belum diimbangi dengan sinergitas branding yang kuat dengan kegiatan event/festival yang digelar atau diselenggarakan oleh Pengelola Desa Wisata Adiluhur. Adanya festival/event yang rutin diselenggarakan sebagai sarana menarik wisatawan agar dapat berkunjung secara rutin. Event yang dibuat nanti harus dapat mengedepankan aspek humanis serta mudah diimplementasikan oleh pengelola Deswita Adiluhur. Adapun Festival yang diimpelementasikan adalah Sport Tourism Festival, English Day Festival, dan Festival Desa Wisata. Ketiga festival tersebut dapat berjalan dengan baik dan mampu mengangkat branding Desa Wisata Adiluhur untuk mendatangkan jumlah wisatawan yang lebih massif.
Pelabelan Sentimen Berbasis Semi-Supervised Learning menggunakan Algoritma LSTM dan GRU Ayuningtyas, Puji; Khomsah, Siti; Sudianto, Sudianto
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 9 No. 3 (2024): September 2024
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2024.9.3.217-229

Abstract

In the sentiment analysis research process, there are problems when still using manual labeling methods by humans (expert annotation), which are related to subjectivity, long time, and expensive costs. Another way is to use computer assistance (machine annotator). However, the use of machine annotators also has the research problem of not being able to detect sarcastic sentences. Thus, the researcher proposed a sentiment labeling method using Semi-Supervised Learning. Semi-supervised learning is a labeling method that combines human labeling techniques (expert annotation) and machine labeling (machine annotation). This research uses machine annotators in the form of Deep Learning algorithms, namely the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. The word weighting method used in this research is Word2Vec Continuous Bag of Word (CBoW). The results showed that the GRU algorithm tends to have a better accuracy rate than the LSTM algorithm. The average accuracy of the training results of the LSTM and GRU algorithm models is 0.904 and 0.913. In contrast, the average accuracy of labeling by LSTM and GRU is 0.569 and 0.592, respectively.
Effectiveness Evaluation of the RandomForest Algorithm in Classifying CancerLips Data Siti Khomsah; Edi Faizal
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 1 (2023): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v1i1.84

Abstract

Lip cancer, though less commonly discussed, remains a significant concern in the realm of oncology. Early detection and diagnosis are paramount for improved patient outcomes. This research evaluated the effectiveness of the RandomForest algorithm in classifying the CancerLips dataset, a collection of lip images processed using the Canny segmentation method and described using Hu moments. Using a 5-fold cross-validation approach, the algorithm achieved an average accuracy of approximately 70.96%. The results highlight the potential of machine learning techniques, specifically RandomForest, in aiding lip cancer detection. However, the choice of preprocessing methods and feature extraction plays a crucial role in determining the outcome. The study underscores the need for further research, focusing on algorithm optimization and comparisons with other datasets or feature extraction methods, to enhance diagnostic precision in medical imaging.
Implementasi Algoritma Catboost Dan Shapley Additive Explanations (SHAP) Dalam Memprediksi Popularitas Game Indie Pada Platform Steam Syamkalla, Mohammad Teddy; Khomsah, Siti; Nur, Yohani Setya Rafika
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 4: Agustus 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.1148503

Abstract

Meningkatnya popularitas game indie di pasar game mewajibkan para pengembang game indie bersaing untuk membuat game nya diminati oleh para pengguna dengan berbagai cara agar dapat meningkatkan potensi popularitasnya. Penelitian sebelumnya telah mencoba menggunakan algoritma logistic regression dan random forest untuk meramalkan popularitas game indie di platform Steam, namun hasil model menggunakan berbagai macam metode masih rendah. Selain itu masih belum memberikan pengetahuan yang cukup kepada pengembang tentang apa yang mempengaruhi popularitasnya.Karena data game indie yang diambil dari platform steam yang digunakan dalam studi ini memiliki tipe kategorikal dan non-linear, maka digunakan pendekatan lain dengan memanfaatkan Algoritma CatBoost yang dalam beberapa penelitian lain terbukti memiliki kinerja dan kemampuan yang lebih baik dalam menangani data kategorikal dan non-linear. Metode Shapley Additive Explanations (SHAP) juga digunakan untuk mengartikan kontribusi dan pengaruh dari setiap fitur terhadap hasil prediksi. Hasil evaluasi pada data game indie dari platform steam hasil scraping yang terdiri dari 52627 baris dan 11 fitur menunjukkan bahwa model CatBoost memiliki akurasi 81%, presisi 0.83, recall 0.77, F1-score 0.80 menunjukkan kemampuan model yang seimbang dalam membedakan kelas popularitas. Hal tersebut didukung dengan nilai AUC 0.88 dimana kurva cenderung mendekati 90 derajat. Metode SHAP mengungkapkan pengaruh fitur terhadap hasil prediksi. Keberadaan kategori steam trading cards, genre RPG dan kompartibel pada sistem operasi mac akan meningkatkan popularitas. Hal tersebut juga terjadi pada semakin tinggi harga dan achievements yang disediakan. Namun keberadaan genre casual akan mengurangi popularitas. Dengan hasil penelitian ini diharapkan dapat membantu pengembang indie dalam mengetahui faktor yang berkemungkinan mempengaruhi popularitas game mereka.   Abstract   The increasing popularity of indie games in the gaming market requires indie game developers to compete to make their games attractive to users in various ways in order to increase their potential popularity. Previous research has tried to use logistic regression and random forest algorithms to forecast the popularity of indie games on the Steam platform, However, the model results using various methods are still low. Since the indie game data taken from the steam platform used in this study is categorical and non-linear, another approach is used by utilizing the CatBoost Algorithm which in several other studies has proven to have better performance and ability in handling categorical and non-linear data. The Shapley Additive Explanations (SHAP) method is also used to interpret the contribution and influence of each feature to the prediction results. Evaluation results on indie game data from the steam platform scraping results consisting of 52627 rows and 11 features show that the CatBoost model has 81% accuracy, precision 0.83, recall 0.77, F1-score 0.80 indicating a balanced model ability in distinguishing popularity classes. This is supported by the AUC value of 0.88 where the curve tends to approach 90 degrees. The SHAP method reveals the influence of features on prediction results. The existence of steam trading cards category, RPG genre and compatibility on mac operating system will increase the popularity. This also happens with the higher prices and achievements provided. However, the presence of the casual genre will reduce popularity. With the results of this study, it is hoped that it can help indie developers in knowing the factors that are likely to affect the popularity of their games.
Class Weighting Approach for Handling Imbalanced Data on Forest Fire Classification Using EfficientNet-B1 Bahtiar, Arvinanto; Hutomo, Muhammad Ihsan Prawira; Widiyanto, Agung; Khomsah, Siti
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 1 (2025): January 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.1.63-73

Abstract

Wildfires pose a threat to ecosystems and human safety, necessitating the development of effective monitoring techniques. Detecting forest fires based on images of forest conditions could be a breakthrough. However, the model built from imbalanced data yields low accuracy. This research addresses the challenge of class imbalance in multiclass classification for forest fire detection using the EfficientNet-B1 model. This research examines the implementation of class weighting to improve model performance, with a particular focus on minority classes, specifically Fire and Smoke. A dataset of 7,331 training images was categorized into four classes. The results showed that employing the class weighting method achieved an accuracy of 90%. The training duration of 14 minutes and 45 seconds outperforms the data augmentation method in terms of time efficiency. This study contributes to the development of more effective methods for forest fire monitoring and provides insights for future research in machine learning applications in environmental contexts.
Comprehensive Lakehouse Data Architecture Model for College Accreditation Nenen Isnaeni; Bambang Purnomosidi Dwi Putranto; Widyastuti Andriyani; Siti Khomsah
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 1 (2025): February
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v5i1.1759

Abstract

Accreditation is an assessment activity that determines the feasibility of study programs at a university. College accreditation data comes from various sources and includes multiple data types: semi-structured, unstructured, or structured. Over time, the volume of data will continue to grow and develop, so there is a possibility of data redundancy and a long time to collect the data needed for accreditation activities. The solution is integrating data. This research aims to design a data architecture to facilitate the management of university accreditation data using the Lakehouse data architecture model. All data types can be stored on one platform in the Lakehouse data architecture. In this research, the identification, integration, and data transformation process for university accreditation data is carried out. The data used in this research is academic data in which there are with. The study's results provide an overview of the data flow process in the Lakehouse data architecture model to help better manage university accreditation data. This architecture also supports real-time data analysis so that the accreditation process can be carried out more effectively and efficiently. Keywords: accreditation, data analysis, data architecture, data lakehouse, data warehouse
Sistem Pakar Deteksi Dini Stunting Balita di Kabupaten Banyumas Menggunakan Metode Certainty Factor Lutfi Hakim, Ahmad; Utami, Annisaa; Khomsah, Siti
eProceedings of Engineering Vol. 12 No. 2 (2025): April 2025
Publisher : eProceedings of Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Balita merupakan tahap kritis dimana sangat rentan mengalami permasalahan gizi, salah satu isu yang kerapmuncul dalam kategori ini adalah stunting, yaitu kondisi yang dipengaruhi oleh berbagai faktor, mulai dari keterbatasan aksesterhadap edukasi di suatu wilayah, minimnya ketersediaan makanan bergizi, kurangnya kesadaran serta pemahamanorang tua terhadap pentingnya asupan nutrisi yang optimal, hingga kendala finansial yang berpengaruh pada keterbatasanpemenuhan kebutuhan gizi anak. Dalam konteks ini, penelitian bertujuan merancang suatu sistem berbasis teknologi yangmampu melakukan deteksi dini terhadap stunting pada balita dengan menerapkan pendekatan certainty factor, sebuahmetode yang digunakan untuk mengukur tingkat kepastian suatu fakta atau aturan berdasarkan bukti yang tersedia.Pengaruh penelitian ini terletak pada urgensinya dalam menangani permasalahan stunting yang dapat membawadampak serius terhadap tumbuh kembang anak, sehingga diperlukan strategi untuk mengidentifikasi gejala stuntingsedini mungkin. Sistem yang dirancang dalam penelitian ini diorientasikan untuk memberikan kemudahan bagi para orangtua dalam melakukan pemantauan terhadap kondisi anak mereka dengan cara mengobservasi perilaku serta tanda-tandayang muncul dalam keseharian balita selama masa pengasuhan. Implementasi dari sistem pakar ini memungkinkan pengguna untuk secara intuitif memilih gejala yang sesuai dengan kondisi balita berdasarkan observasi, yang kemudian diproses menggunakan perhitungan certainty factor guna menghasilkan estimasi tingkat kemungkinan stunting pada anak. Sebagai output dari penelitian ini, dikembangkan suatu sistem pakar dalam format berbasis web yang dirancang secara interaktif, sehingga dapat diakses dengan mudah oleh pengguna, memungkinkan mereka untuk melakukan analisis gejala secara fleksibel dengan memasukkan tingkat keyakinan terhadap gejala yang diamati, serta memperoleh hasil akhir berupaperhitungan prediktif berbasis certainty factor. Dengan hadirnya sistem ini, dapat tercipta suatu solusi praktis danefektif sebagai alat bantu yang dapat digunakan dalam upayapencegahan stunting sejak dini, memberikan wawasan yanglebih akurat bagi para orang tua dalam mengambil langkahlangkah yang diperlukan demi memastikan optimalisasipertumbuhan dan perkembangan balita.Kata kunci4 Certainty Factor, Stunting, Web
Penerapan Feature Engineering dan Hyperparameter Tuning untuk Meningkatkan Akurasi Model Random Forest pada Klasifikasi Risiko Kredit Nur Fauzi, Nadea Putri; Khomsah, Siti; Putra Wicaksono, Aditya Dwi
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 2: April 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025128472

Abstract

  Risiko kredit adalah hal yang penting untuk dianalisis di awal pengajuan kredit guna mengurangi nilai Non-Performing Loan (NPL) atau risiko gagal bayar. Pola pengetahuan risiko kredit bisa diketahui dari data-data historikal sehingga data pengajuan kredit baru bisa ketahui risikonya lebih awal. Pada penelitian-penelitian terdahulu, model klasifikasi untuk risiko kredit menggunakan Random Forest banyak ditemukan namun tidak mendalam dalam penerapan preprocessing dan akurasinya masih rendah. Maka penelitian ini bertujuan meningkatkan akurasi model klasifikasi algoritma Random Forest dengan menerapkan tuning parameter dan feature engineering yang lebih dalam. Metodologi penelitian yang digunakan adalah Sample, Explore, Modify, Models, dan Assess (SEMMA). Penelitian ini menerapkan berbagai kombinasi parameter dan menerapkan feature engineering untuk memperbaiki kualitas data. Feature engineering yang digunakan meliputi oversampling dan standardisasi. Hyperparameter tuning model Random Forest menggunakan metode Random Search dan Grid Search untuk mencari parameter paling optimal. Dataset penelitian adalah data sekunder (Credit Risk) yang terdiri dari 32.581 baris, 11 variabel prediktor dan 1 variabel respon. Hasil penelitian menunjukkan penerapan feature engineering signifikan meningkatkan akurasi model Random Forest, meningkat dari 92,56% menjadi 97,94% setelah menerapkan oversampling dan standarisasi. Sedangkan hyperparameter tuning tidak begitu signifikan meningkatkan akurasi model yang dibangun menggunakan dataset yang sudah dikenakan preprocessing maupun feature engineering dengan baik.   Abstract Credit risk analysis is essential for minimizing the value of non-performing loans (NPL). Using historical data to understand credit risk patterns can help identify risks early in new credit applications. Previous research has often used Random Forest classification models for credit risk but found the need for more comprehensive preprocessing of applications and higher accuracy. This research aims to improve the accuracy of the Random Forest algorithm classification model by implementing parameter tuning and feature engineering. The SEMMA (Sample, Explore, Modify, Model, and Assess) methodology is used, which explores different parameters and feature engineering combinations to enhance data quality. Feature engineering techniques, such as oversampling and standardization, are applied. Hyperparameter tuning of the Random Forest model involves using Random Search and Grid Search methods to identify the optimal parameters. The research dataset, consisting of 32.581 lines, 11 predictor variables, and one response variable, is secondary data on Credit Risk. Results show that the application of feature engineering significantly improves the accuracy of the Random Forest model, increasing from 92,56% to 97,94% after applying oversampling and standardization. However, hyperparameter tuning does not significantly increase the accuracy of models built using well-preprocessed datasets or feature engineering.
PEMBERDAYAAN DAN PENINGKATAN KAPASITAS KELEMBAGAAN MASYARAKAT DESA MELALUI AGROWISATA BERBAHASA INGGRIS Nugraha, Novanda Alim Setya; Khomsah, Siti; Ramadhani, Rima Dias; Laksana, Tri Ginanjar
Devote: Jurnal Pengabdian Masyarakat Global Vol. 1 No. 2 (2022): Devote : Jurnal Pengabdian Masyarakat Global, Desember 2022
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (654.782 KB) | DOI: 10.55681/devote.v1i2.402

Abstract

Agribusiness utilizes inorganic waste that can be recycled to produce selling points such as basket bags from plastic waste and dolls from cloth waste. There is a superior product in this business unit, namely processed Jenitri seeds which are processed into handicrafts. Jenitri seeds are not only used as an ornamental tool. In certain beliefs, the work of Jenitri seeds is used as a worship tool and a medical device. Therefore, Jenitri seeds have a fairly high selling value compared to other handicrafts because of their various functions. Adiluhur Tourism Village is a village that is currently under development as a tourist spot with the name Kebumen English Tourism Village (KWIK) and is located in Adiluhur Village, Kec. Adimulyo, Kab. Kebumen. There are 3 business units that are superior and are still in the development stage, namely business units in the fields of tourism (agro-tourism), agriculture (agriculture), and handicrafts (agribusiness). Currently, the primary superior unit in Adiluhur Tourism Village is a business unit in the tourism sector. Agrotourism is managed by CV in collaboration with BUMDes (Village Owned Enterprise) Mulia Jaya. The tour featured in this unit is an introduction to several types of captive animals (various types of snakes, monitor lizards, sea urchins, iguanas, mongooses, Australian geckos, crocodiles, alligator fish, and many more) as well as a museum containing ancient agricultural tools (bronze spoon, harrows, sickles, hoes, nails, antique lamps, and many more). The potential that is being developed in this unit is outbound with the target visitor being Elementary Schools. Not only that, the agro-tourism manager plans to work with the BKSDA (Natural Resources Conservation Center) in caring for the animals in the unit.
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
Co-Authors Aditya Dwi Putro Aditya Dwi Putro Wicaksono Agus Sasmito Aribowo Agus Sasmito Aribowo Agus Sasmito Aribowo Agus Sasmito Aribowo Alam Patria Utama Annisa Ferani Tanjung, Nia Ardian, Miko 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 Lutfi Hakim, Ahmad M Yoka Fathoni Mufidah, Lulu Nenen Isnaeni Noor Fazilla Abd Yusof Novanda Alim Setya Nugraha Nur Fauzi, Nadea Putri Nur, Yohani Setya Rafika Putra Wicaksono, Aditya Dwi Rahmadani, Dea Caesy Ramadhani, Rima Dias Ridwan Pandiya Rona Nisa SA Sausan Sena Wijaya Sena Wijayanto Sudianto Syamkalla, Mohammad Teddy Tri Ginanjar Laksana Ummi Athiyah Utami, Annisaa Wenny Marlini Widiyanto, Agung Widyastuti Andriyani Zundina Ulya, Fadilla