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All Journal Jurnal Transformatika JOIN (Jurnal Online Informatika) JTERA (Jurnal Teknologi Rekayasa) Jurnal Sistem Cerdas Building of Informatics, Technology and Science JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Informatika dan Teknologi Komputer ( J-ICOM) JURNAL ELEKTRO DAN INFORMATIKA SWADHARMA (JEIS) Journal of Dinda : Data Science, Information Technology, and Data Analytics JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Informatika: Jurnal Pengembangan IT The Indonesian Journal of Computer Science JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Harani, Nisa Hanum
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering

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Penerapan Adaboost Berbasis Pohon Keputusan Guna Menentukan Pola Masuknya Calon Mahasiswa Baru Harani, Nisa Hanum
Jurnal Transformatika Vol. 18 No. 1 (2020): July 2020
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v18i1.1606

Abstract

In general, the college admission process is done through registration, file selection, examinations, an announcement of the results of students who pass, and ends with re-registration. In this case, a problem was found where there is a significant decrease in the number of student who register with those who re-register .Things like this can reduce the balance between new students and students who meet the requirements, to make a decrease in the quality of higher education and affect accreditation. Based on these problems, a classification method was developed to look for patterns of students who would enter institutions and what factors influence students to re-register.To improve the accuracy of the decision tree algorithm the author use adaptive boosting (adaboost) in finding factors that make prospective students continue to the re-registration process.From the results of the study, the AdaBoost-based decision tree algorithm shows that the level of accuracy has an increase of 20%. The presentation of results is as follows, 61.4% (decision tree); 91.35% (decision tree + AdaBoost)
Implementasi Algoritma C5.0 untuk menentukan Pelanggan Potensial di Kantor Pos Cimahi Harani, Nisa Hanum; Rahayu, Woro Isti; Damayanti, Fanny Shafira
Jurnal Transformatika Vol. 19 No. 2 (2022): January 2022
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v19i2.3098

Abstract

Kantor Pos Cimahi merupakan perusahaan BUMN yang bergerak pada bidang jasa pengiriman barang. Saat ini banyak perusahaan swasta yang bergerak dalam bidang jasa pengiriman barang, sehingga menyebabkan banyaknya pesaing bagi Kantor Pos Cimahi dan dapat menyebabkan pelanggan yang menggunakan jasa Kantor Pos Cimahi berkurang. Oleh karena itu diperlukan suatu sistem yang dapat membantu Kantor Pos Cimahi untuk dapat menentukan pelanggan potensial agar dapat diketahui pelanggan mana yang potensial sehingga dapat diberikan perlakuan khusus agar pelanggan tersebut tetap menggunakan jasa Kantor Pos Cimahi. Sistem yang dibangun menggunakan bahasa pemrograman PHP dan metode Algoritma C 5.0 yang merupakan salah satu algoritma pohon keputusan yang dapat membantu untuk menentukan pelanggan potensial. Penelitian menggunakan data transaksi periode bulan januari oktober 2020 dimana atribut yang digunakan yaitu bulan, nama perusahaan, jenis kiriman yang digunakan, jumlah transaksi selama sebulan, dan total uang. Hasil penelitian menunjukan bahwa algoritma C 5.0 mampu melakukan menentukan data pelanggan potensial dengan akurasi sebesar 96%.
Enhancing Prediction Accuracy of the Happiness Index Using Multi-Estimator Stacking Regressor and Web Application Integration Zain, Rofi Nafiis; Harani, Nisa Hanum; Pane, Syafrial Fachri
Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 2 (2025): August
Publisher : Research Group of Data Engineering, Faculty of Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/dinda.v5i2.1871

Abstract

This study proposes a novel approach to enhance the prediction accuracy of the Happiness Index using a multi-estimator stacking regressor model and web application integration. By combining diverse regression models, such as decision tree, random forest, gradient boosting, LGBM, and support vector regressor (SVR), the proposed ensemble architecture achieved superior predictive performance with an score of 0.9814. A custom Happiness Score was formulated using weighted indicators derived from Pearson’s correlation analysis. Furthermore, SHapley Additive exPlanations (SHAP) were used to interpret model predictions, revealing the Human Development Index, Female Labour Force Rate, and Life Expectancy as key contributing features. The final model was deployed via a Python Flask-based web dashboard, enabling stakeholders to visualize happiness metrics interactively. The results suggest that stacking-based regression, when combined with interpretability techniques and real-time deployment, can offer a powerful solution for socioeconomic modeling and supporting urban policy.
Simulasi Dinamis Single Qubit dan Multi Qubit: Sebuah Pendekatan Python Setyawan, Muhammad Yusril Helmi; Harani, Nisa Hanum; Andriyanto, Achmad
Jurnal Teknik Informatika dan Sistem Informasi Vol 11 No 2 (2025): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v11i2.10075

Abstract

This study developed a dynamic simulation system for single qubit and multi qubit using a Python-based approach, leveraging quantum computing libraries such as Qiskit, NumPy, and Matplotlib. The system is designed to simulate various quantum operations, including Hadamard, Pauli-X, Pauli-Y, Pauli-Z, CNOT, and Toffoli, with integration into a Flask-based web interface for easy user interaction. The simulation results show a high level of accuracy, with a difference of only 0.2% in measurement probabilities for single qubit operations like Hadamard and less than 0.4% for multi qubit operations like CNOT and Toffoli. The tests also demonstrated efficient execution times, ranging from 12 to 25 milliseconds, even for complex quantum operations. Validation against established literature confirms that the system is accurate, efficient, and reliable, making it a valuable tool for supporting learning and research in quantum computing.
Feature Selection and Reduction in Happiness Index Analysis: A Systematic Literature Review Ferdinan, Dani; Harani, Nisa Hanum
Jurnal Sistem Cerdas Vol. 8 No. 2 (2025)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v8i2.540

Abstract

This study investigates the role and effectiveness of feature selection and feature reduction techniques in improving the accuracy, validity, and efficiency of predictive models for survey-based happiness indices. A Systematic Literature Review (SLR) was conducted following the PRISMA 2020 protocol, evaluating 40 peer-reviewed articles published between 2020 and 2025. The results demonstrate that feature selection methods namely wrapper, filter, and embedded approaches can significantly enhance model performance, yielding higher coefficients of determination (R²) and lower prediction errors. Furthermore, the identification of relevant features has been shown to improve construct validity and the reliability of happiness indicators. The integration of feature selection and feature reduction techniques also contributes to more efficient and stable models, particularly in high-dimensional data contexts. However, the limited number of studies directly addressing happiness and the methodological heterogeneity across works pose challenges to the generalizability of the findings. This review provides valuable insights for establishing evidence-based practices and guiding strategic developments in future happiness index analytics
Pendekatan LSTM Berbasis Deep Learning dalam Memprediksi Fluktuasi Harga Cabai Pertiwi, Aryka Anisa; Harani, Nisa Hanum; Prianto, Cahyo
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8100

Abstract

The significant fluctuation in chili prices in Indonesia leads to economic instability, particularly for consumers and market stakeholders. This study aims to develop a daily chili price prediction model using the Long Short-Term Memory (LSTM) algorithm based on deep learning, designed to capture seasonal patterns and long-term dependencies in historical data. The research adopts the CRISP-DM approach, encompassing business understanding, data processing, model training, and implementation into a web-based dashboard. The dataset, collected from Pagar Alam City between 2022 and 2024, includes features such as previous prices, chili sub-variants, sinusoidal time transformations, and market conditions. The LSTM regression model demonstrated high performance, achieving an R² score of 0.9567, a MAE of 1,402.92, and an RMSE of 2,595.98. Additionally, a classification model was developed to predict price status (increase, decrease, stable) as a decision-support tool. The deployment of this system into an interactive dashboard enables real-time price predictions. These results indicate that the LSTM-based approach is not only technically accurate but also offers a practical solution for commodity price monitoring and decision-making in the food sector.
Stock Price Prediction Using LSTM and XGBoost with Social Media Sentiment Harani, Nisa Hanum; Marismati, Marismati
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.8284

Abstract

The influence of social media on financial markets is growing and motivates research on the predictive role of sentiment in stock price movements. Bank Negara Indonesia (BBNI) is part of the Danantara holding company, and BBNI's strategic position is an important indicator for measuring the performance of the broader financial ecosystem in Indonesia. This study analyzes the influence of social media sentiment on the stock price prediction of Bank Negara Indonesia (BBNI), which is part of the state-owned holding company Danantara. Historical market data is combined with sentiment indicators obtained from public conversations on X/Twitter. Daily sentiment features are then integrated with market variables, including OHLCV data, to form a combined dataset. Two machine learning approaches were employed: Long Short-Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost). The results revealed contrasting patterns between the two models. The LSTM Baseline consistently produced RMSE around (≈46–65) across all scenarios. However, XGBoost-Extended is the best-performing and recommended model for sentiment-integrated prediction with RMSE (≈30–40).
Tinjauan Literatur: Deteksi Kanker Serviks Dengan Pendekatan Machine Learning Hutapea, Juwita Stefany; Harani, Nisa Hanum
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 13, No 4 (2025)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v13i4.95910

Abstract

Kanker serviks merupakan salah satu jenis kanker yang masih menjadi penyebab utama kematian pada wanita di seluruh dunia. Penyakit ini berkembang secara perlahan dan sering kali tidak menunjukkan gejala pada tahap awal, sehingga deteksi dini menjadi kunci utama dalam upaya pencegahan dan pengobatan. Saat ini, metode deteksi seperti Pap smear dan tes HPV telah banyak digunakan. Namun keterbatasan sumber daya medis dan tantangan dalam akurasi diagnosis masih menjadi hambatan. Dengan kemajuan teknologi, algoritma machine learning mulai dimanfaatkan untuk mendukung proses deteksi kanker serviks secara cepat dan akurat. Penelitian ini bertujuan untuk meninjau secara sistematis penerapan algoritma machine learning dalam deteksi kanker serviks melalui pendekatan Systematic Literature Review (SLR). Sebanyak 243 artikel diidentifikasi dan 42 artikel dipilih untuk dianalisis menggunakan metode PRISMA yang mencakup tahapan identifikasi, penyaringan, evaluasi kelayakan, dan inklusi akhir. Analisis dilakukan terhadap algoritma yang digunakan, variabel prediktor, teknik seleksi fitur, serta jenis dan ukuran dataset. Hasil tinjauan menunjukkan bahwa dataset UCI Cervical Cancer, SEER Database, dan Herlev Dataset merupakan yang paling sering digunakan dengan ukuran bervariasi dari 92 hingga lebih dari 381.000 data. Variabel usia, penggunaan kontrasepsi, dan jumlah pasangan seksual merupakan indikator yang paling sering muncul. Model yang paling banyak diterapkan adalah Random Forest, Decision Tree, Support Vector Machine (SVM), XGBoost, dan Multilayer Perceptron (MLP) dengan akurasi berkisar 82% - 100% yang menunjukkan performa tinggi terutama setelah dilakukan tuning. Selain itu, metode seleksi fitur seperti Chi-Square, LASSO, dan Principal Component Analysis (PCA) berkontribusi dalam meningkatkan akurasi model. Walaupun hasilnya menunjukkan potensi yang baik, penelitian yang ditinjau masih terbatas, seperti ketidakseimbangan kelas, kurangnya validasi eksternal, dan perbedaan metode evaluasi yang memengaruhi kemampuan generalisasi model. Penelitian selanjutnya disarankan untuk memanfaatkan dataset yang lebih beragam, menerapkan metode penyeimbangan data yang lebih baik, serta memperluas validasi pada populasi yang berbeda guna meningkatkan keandalan deteksi dini kanker serviks berbasis machine learning.
Sentiment Analysis on Social Distancing and Physical Distancing on Twitter Social Media using Recurrent Neural Network (RNN) Algorithm Nugraha, Fikri Aldi; Harani, Nisa Hanum; Habibi, Roni; Fatonah, Rd. Nuraini Siti
JOIN (Jurnal Online Informatika) Vol. 5 No 2 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i2.632

Abstract

The government is seeking preventive steps to reduce the risk of the spread of Covid-19, one of which is social restrictions that have become popular with social distancing and physical distancing. One way to assess whether the steps taken by the government regarding social and physical distancing are accepted or not by the community is by conducting sentiment analysis. The process of sentiment analysis is carried out using a variant of the Recurrent Neural Network (RNN), namely Long Short-Term Memory (LSTM). In this study, the results obtained from the sentiment analysis, where the public response to social distancing and physical distancing has more positive sentiments than negative sentiments. To measure the accuracy level of sentiment analysis using the Recurrent Neural Network (RNN) algorithm and evaluation of the modeling is done using confusion matrix where the results obtained for the training dataset are 89% accuracy, 89% recall, 89% precision, and 89% F1 Score. Meanwhile, for the test dataset, an accuracy of 80% was obtained, a recall of 79%, a precision of 81%, and an F1 score of 80%.
ANALISIS PERBANDINGAN ENSEMBLE MACHINE LEARNING DENGAN TEKNIK SMOTE UNTUK PREDIKSI DIABETES Adiningrum, Nur Tri Ramadhanti; Harani, Nisa Hanum
JEIS: Jurnal Elektro dan Informatika Swadharma Vol 5, No 1 (2025): JEIS EDISI JANUARI 2025
Publisher : Institut Teknologi dan Bisnis Swadharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56486/jeis.vol5no1.681

Abstract

High blood glucose levels characterize a chronic disease called diabetes. Patients with diabetes will eventually experience health problems. These cases show that early detection and better diagnosis are needed. Although several Machine Learning (ML) models have been widely used in diabetes diagnosis, the algorithm performance is still between 70 - 79%. This study evaluates the use of Ensemble Machine Learning to predict diabetes using the Pima Indian Diabetes dataset. The models compared are Support Vector Machine, Linear Regression, Naive Bayes, Random Forest, AdaBoost, K Nearest Neighbour, and Decision Tree. The dataset will also be balanced using the Synthetic Minority Over-sampling Technique (SMOTE) to reduce accuracy bias. Cross-Industry Standard Process For Data Mining (CRISP-DM) is the methodology used. The accuracy results show that Random Forest with Bagging and Hard-Voting produces the best accuracy of other models. Where Random Forest produces an accuracy of 81.16% and Hard-Voting also produces an accuracy of 81.16%.Penyakit kronis yang disebut diabetes ditandai dengan kadar glukosa darah yang tinggi. Pasien dengan diabetes pada akhirnya akan mengalami masalah kesehatan. Kasus-kasus ini menunjukkan bahwa deteksi dini dan diagnosis yang lebih baik diperlukan. Meskipun beberapa model Machine Learning (ML) telah banyak digunakan dalam diagnosis diabetes, kinerja algoritmanya masih antara 70 - 79%. Untuk memutuskan apakah seseorang menderita diabetes atau tidak, penelitian ini mengevaluasi penggunaan Ensemble Machine Learning untuk memprediksi diabetes menggunakan dataset Diabetes Pima Indian. Model yang dibandingkan adalah Support Vector Machine, Linear Regression, Naive Bayes, Random Forest, Adaboost, K Nearest Neighbor, dan Decision Tree. Untuk mengurangi bias akurasi, dataset juga akan diseimbangkan menggunakan Synthetic Minority Over-sampling Technique (SMOTE). Cross-Industry Standard Process For Data Mining (CRISP-DM) adalah metodologi yang digunakan. Hasil akurasi menunjukkan bahwa Random Forest dengan Bagging dan Hard-Voting menghasilkan akurasi terbaik dari model lainnya. Dimana Random Forest menghasilkan akurasi sebesar 81,16% dan Hard-Voting juga menghasilkan akurasi sebesar 81,16%.
Co-Authors Adiningrum, Nur Tri Ramadhanti Andarsyah, Roni Andriyanto, Achmad Annisah, Wulan Nur Damayanti, Fanny Shafira Fatonah, Rd. Nuraini Siti Ferdinan, Dani Habibi, Roni Hutapea, Juwita Stefany Ilyas Tri Khaqiqi, M Latifah, Isnaenti Nur Marismati, Marismati Nugraha, Fikri Aldi Pertiwi, Aryka Anisa Prianto, Cahyo Rahayu, Woro Isti Salsabila, Helmi Setyawan, Muhammad Yusril Helmi Syafrial Fachri Pane, Syafrial Fachri Tri Khaqiqi, M. Ilyas Trigreisian, Alwizain Almas Zain, Rofi Nafiis Zuhri, Burhanudin