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SISTEM INFORMASI MONITORING MUTABA’AH MENGGUNAKAN METODE AGILE EXTREME PROGRAMMING PADA YAYASAN DAARUT TAUHIID Rizki, Muhamad; Fauziah, Fauziah; Sholihati, Ira Diana
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 1 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i1.4326

Yayasan Daarut Tauhiid Bandung merupakan lembaga nirlaba yang memberikan keuntungan melalui program pelayanan dan pemberdayaan di bidang ekonomi, kesehatan, pendidikan, dakwah dan sosial kemanusiaan. Yayasan ini memberikan pembelajaran kepada para santri tentang keagamaan. Pada pembelajaran itu santri juga diberikan tugas akan kegiatan yang mereka lakukan seperti ibadah yang dapat membantu mengontrol dan mengelola ibadah dengan cara meriwayatkan ibadah harian yang telah dilakukan. Namun dalam pencatatannya itu masih menggunakan cara manual dengan input pada excel hari demi hari dari para santri maupun oleh pengajar sehingga memakan banyak waktu juga data yang bisa hilang. Hal tersebut menjadi acuan bagi peneliti untuk melakukan perancangan sistem informasi monitoring mutaba’ah.Penelitian ini untuk menyiapkan suatu sistem menggunakan metode Agile model Extreme Programming. Dalam pengujian website sistem informasi monitoring mutaba’ah ini menggunakan PageSpeed Insights untuk performa web dan Apache JMeter untuk database. Pengujian performa web menghasilkan informasi dengan nilai Performance 99%, Accessibility 81%, Best Practices 92%, dan SEO 82%, serta pengujian database yang dapat mendukung jalannya aplikasi menghasilkan tingkat keberhasilan 100% dengan rincian kegagalan 0 dan rata-rata respon waktu untuk menampilkan data selama 1,34ms.dan SEO 82%, serta pengujian database yang dapat mendukung jalannya aplikasi menghasilkan tingkat keberhasilan 100% dengan rincian kegagalan 0 dan rata-rata respon waktu untuk menampilkan data selama 1,34ms.dan SEO 82%, serta pengujian database yang dapat mendukung jalannya aplikasi menghasilkan tingkat keberhasilan 100% dengan rincian kegagalan 0 dan rata-rata respon waktu untuk menampilkan data selama 1,34ms.

E-Recruitment Menggunakan Metode Simple Additive Weighting dan Algoritma K-Nearest Neighbor Janubiya, Tasya Khaerani; Andryana, Septi; Sholihati, Ira Diana
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 1 (2022): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i1.434

Along with the increasing number of PT Midi Utama Indonesia Tbk outlets, the company's human resource needs are also increasing. Therefore, the recruitment of new employees is very important to support the company's operations. In order to select prospective new employees to fill various positions needed by the company. Recruitment of new employees has not been carried out professionally. This is because there is no systematic method to assess the suitability of new employees. The application of a decision support system uses a combination of the Simple Additive Weighting (SAW) method and the K-Nearest Neighbor (K-NN) algorithm. This method determines the weight value and ranking results of each candidate. Then, the company conducts the process of selecting candidate data based on the value closest to the old prospective employee to determine the final classification results. In this case, prospective new employees who qualify as employees are based on predetermined criteria. Then this decision support application is built using the PHP programming language and MySQL database. The conclusion in this study by combining the SAW and K-NN methods in the recruitment process is very helpful because the administrative and assessment processes are carried out online. So that decision makers can make choices quickly and accurately.

E-Recruitment Menggunakan Metode Simple Additive Weighting dan Algoritma K-Nearest Neighbor Janubiya, Tasya Khaerani; Andryana, Septi; Sholihati, Ira Diana
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 1 (2022): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i1.434

Along with the increasing number of PT Midi Utama Indonesia Tbk outlets, the company's human resource needs are also increasing. Therefore, the recruitment of new employees is very important to support the company's operations. In order to select prospective new employees to fill various positions needed by the company. Recruitment of new employees has not been carried out professionally. This is because there is no systematic method to assess the suitability of new employees. The application of a decision support system uses a combination of the Simple Additive Weighting (SAW) method and the K-Nearest Neighbor (K-NN) algorithm. This method determines the weight value and ranking results of each candidate. Then, the company conducts the process of selecting candidate data based on the value closest to the old prospective employee to determine the final classification results. In this case, prospective new employees who qualify as employees are based on predetermined criteria. Then this decision support application is built using the PHP programming language and MySQL database. The conclusion in this study by combining the SAW and K-NN methods in the recruitment process is very helpful because the administrative and assessment processes are carried out online. So that decision makers can make choices quickly and accurately.

Implementation Convolutional Neural Network for Visually Based Detection of Waste Types Wedha, Bayu Yasa; Sholihati, Ira Diana; Ningsih, Sari
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3427

Waste detection plays an essential role in ensuring efficient waste management. Convolutional Neural Networks are used in visual waste detection to improve waste management. This study uses a data set that covers various categories of waste, such as plastic, paper, metal, glass, trash, and cardboard. Convolutional Neural Networks are created and trained with refined architecture to achieve precise classification results. During the model development stage, the focus is on utilizing transfer learning techniques to implement Convolutional Neural Networks. Utilizing pre-trained models will speed up and improve the learning process by enriching the representation of waste features. By using the information embedded in the trained model, the Convolutional Neural Network can differentiate the specific attributes of various waste categories more accurately. Utilizing transfer learning allows models to adapt to real-world scenarios, thereby improving their ability to generalize and accurately identify waste that may exhibit significant variation in appearance. Combining these methodologies enhances the ability to identify waste in diverse environmental conditions, facilitates efficient waste management, and can be adapted to contemporary needs in environmental remediation. The model evaluation shows satisfactory performance, with a recognition accuracy of about 73%. Additionally, experiments are conducted under authentic circumstances to assess the reliability of the system under realistic circumstances. This study provides a valuable contribution to the advancement of waste detection systems that can be integrated into waste management with optimal efficiency.

Online Tutoring's Technological Foundation and Future Prospects: Enterprise Architecture Development Ningsih, Sari; Wedha, Bayu Yasa; Sholihati, Ira Diana
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3433

This study examines the advancement of enterprise architecture with the objective of enhancing the technological infrastructure and long-term strategies in the online student tutoring sector. Online tutoring has emerged as the primary option for supporting the learning process in the rapidly advancing digital age. Identify the essential elements involved in establishing robust groundwork for an online tutoring platform, with a focus on highlighting the strategic significance of enterprise architecture. Examining the technological infrastructure that is customized to fulfill the demands of the tutoring sector constitutes the research methodology utilized in this investigation. Enterprise architecture serves as the fundamental framework that enables smooth integration among different systems, applications, and services used in online tutoring. Creating an enterprise architecture will subsequently generate a well-defined technology roadmap, empowering tutoring companies to innovate with greater precision. This architecture enhances the role of online tutoring in providing a more adaptable and personalized learning experience for students by utilizing advanced technologies like artificial intelligence and data analytics. This study emphasizes the significance of enterprise architecture in facilitating educational transformation and establishing a robust framework for online tutoring companies to progress efficiently. To foster the growth and advancement of the online tutoring industry, it is crucial to strategically enhance the technological infrastructure and implement a well-designed enterprise architecture. This will enable the sector to play a substantial role in shaping a dynamic and forward-thinking educational landscape.

Comparison Of Feature Extraction Techniques For Long Short-Term Memory Models In Indonesian Automatic Speech Recognition Armaisya, Dimas Dwi; Pamungkasari, Panca Dewi; Rifai, Achmad Pratama; Sholihati, Ira Diana; Gopal Sakarkar
Green Intelligent Systems and Applications Volume 5 - Issue 1 - 2025
Publisher : Tecno Scientifica Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53623/gisa.v5i1.605

Automatic Speech Recognition (ASR) faced challenges in accuracy and noise robustness, particularly in Bahasa Indonesia. This research addressed the limitations of single feature extraction methods, such as Mel-Frequency Cepstral Coefficients (MFCC), which were sensitive to noise, and Relative Spectral Transform - Perceptual Linear Predictive (RASTA-PLP), which was less effective in frequency representation, by proposing a hybrid approach that combined both techniques using Long Short-Term Memory (LSTM) models. MFCC enhanced spectral accuracy, while RASTA-PLP improved noise robustness, resulting in a more adaptive and informative acoustic representation. The evaluation demonstrated that the hybrid method outperformed single and non-extraction approaches, achieving a Character Error Rate (CER) of 0.5245 on clean data and 0.8811 on noisy data, as well as a Word Error Rate (WER) of 0.9229 on clean data and 1.0015 on noisy data. Although the hybrid approach required longer training times and higher memory usage, it remained stable and effective in reducing transcription errors. These findings suggested that the hybrid method was an optimal solution for Indonesian speech recognition in various acoustic conditions.

Grid-Based Ship Density Analysis and Anomaly Detection for Ship Movements Monitoring at Tanjung Priok Port Ikhsan, Muhammad Ramadhan; Pamungkasari, Panca Dewi; Purbantoro, Babag; Sholihati, Ira Diana; Farahdinna, Frenda; Sumantyo, Josaphat Tetuko Sri; Heezen, Damy Matheus
International Journal of Advances in Data and Information Systems Vol. 6 No. 1 (2025): April 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i1.1367

Indonesia, as a maritime country, depends on ports to support inter-island transport and a smooth regional economy. So, the awareness of knowing the marine status with various platforms is needed. This research distinguishes itself from several previous studies on ship movement detection by concentrating specifically on anomalies in ship movement within areas of high traffic density. This research proposes to find out the ship density area using the grid technique and identify the anomalies that have occurred, as information on ship movements at Tanjung Priok Port. Anomaly detection is done by looking for it through visualization, where AIS data is converted into a form of visualization using the Python language. The results obtained two pieces of information, namely that the areas with the highest density are around the harbor, docks, and ship lanes. Then, two types of anomalies were detected, namely large ships with dangerous cargo speeding in dense areas and ships that behave differently compared to other ships with the same status.

Integration of Machine Learning and Blockchain for Forest Fire Risk Prediction Ramadhani, Nursetiaji; Sholihati, Ira Diana; Hindarto, Djarot
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.15435

This study presents an integrated framework combining machine learning and blockchain technology to enhance the accuracy, transparency, and reliability of forest fire risk prediction in tropical regions. Using geospatial and climatological datasets from Google Earth Engine (GEE), two ensemble algorithms—Random Forest (RF) and Extreme Gradient Boosting (XGBoost)—were trained to model spatial fire susceptibility based on variables such as temperature, humidity, rainfall, wind speed, and vegetation index (NDVI). The RF model effectively identified low-risk areas but was less sensitive to minority high-risk classes, while XGBoost demonstrated superior adaptability in handling class imbalance and achieved more balanced performance across all categories. To ensure data authenticity and traceability, the prediction results were validated and recorded on the Ethereum blockchain using smart contracts. Each prediction output was transformed into a cryptographic hash (SHA-256) to guarantee immutability and verifiability. The integration of machine learning with blockchain establishes a decentralized, tamper-proof, and verifiable prediction system, promoting data integrity and transparency in environmental monitoring. Overall, this research introduces a novel “verifiable prediction pipeline” that advances both artificial intelligence and blockchain applications in environmental informatics, supporting proactive and accountable forest fire mitigation strategies.

Metode K-Nearest Neighbor Dan Naive Bayes Dalam Menentukan Status Gizi Balita Pratama, Junius; Fauziah, F; Sholihati, Ira Diana
Brahmana : Jurnal Penerapan Kecerdasan Buatan Vol 4, No 2 (2023): Edisi Juni
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/brahmana.v4i2.197

The nutritional intake needed by toddlers during the growth period for each individual has a different amount of consumption, therefore a process of checking nutritional status must be carried out. The indicators high and weight that will be calculated in determining the nutritional status. Until now the process of it status children under five is still carried out manually, resulting in the classification process of nutritional not being as expected. It is difficult for parents to go to the health center to check on their child's condition because the location is far from where they live and the administrative process is long. The propose is website-based information system, as well as providing recommendations on which method is the most accurate among K-Nearest Neighbors and Naïve Bayes in determining the nutritional status of toddlers. The results of this study have succeeded in creating an information system that can be used by users by inputting condition criteria for the process of classifying the nutritional status of toddlers. Naïve Bayes method is superior with a percentage value of 87.5% while the K-Nearest Neighbor method with k = 3 has an accuracy percentage of 71.25%

Perancangan User Interface/User Experience Food Delivery App Dengan Menggunakan Metode Design Thinking Ramdhan, Muhammad Yanuar; Nurhayati, Nurhayati; Sholihati, Ira Diana
e-Jurnal JUSITI (Jurnal Sistem Informasi dan Teknologi Informasi) Vol. 13 No. 2 (2024): e-Jurnal JUSITI
Publisher : Universitas Dipa Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36774/jusiti.v13i2.1636

Springplate adalah Aplikasi Restoran yang dimana konsumen bisa memesan makanan melalui aplikasi. Springplate juga menyediakan catalog food, best menu, dan juga transaksinya. Tujuan Springplate adalah mempermudah user untuk pemesanan makanan dari jarak jauh dan membuat beberapa layanan pemesanan makanan secara delivery yang lebih banyak serta sesuai dengan kebutuhan pengguna. Penelitian pembelian makanan restoran online ini bertujuan untuk membangun aplikasi Springplate mempermudah para konsumen, dimana para konsumen tidak perlu untuk datang ketempat hanya untuk melakukan pemesanan, tapi dengan adanya aplikasi ini dapat langsung melakukan pemesanan makanan secara langsung lewat aplikasi yang tersedia. Proyek ini menggunakan metode Design Thinking dan In Depth Interview untuk menyelesaikan desain aplikasi Springplate. Dari hasil pengujian yang ada dibuktikan bahwa desain solusi mendapatkan nilai 70 yang menunjukkan tingkat kepuasan, kemudahan dan kegunaan dari Prototype ini sudah baik. Namun masih ada beberapa fitur yang masih bisa lengkapi agar aplikasi bisa berkembang lebih baik lagi.

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