Abstract:Abstract: The increasing use of digital banking applications has led to the need for a deeper understanding of user perceptions, especially through aspect-based sentiment analysis. This study aims to classify the sentiment…
nt of SeaBank app users by focusing on four main aspects: learnability, efficiency, technical issues or errors, and satisfaction. Review data totaling 1,971 comments were collected from the Google Play Store and labeled with sentiments based on the scores (ratings) given by users. The CRISP-DM approach serves as the methodological framework for this study, which includes five classification algorithms: Naïve Bayes, Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Tree, and Random Forest. The evaluation results show that the SVM algorithm provides the best performance with the highest average value of the four aspects achieving accuracy of 93.91%, Precision of 91.16%, recall of 97.96% and F1-Measure of 94.33%. According to the research findings, the Support Vector Machine (SVM) algorithm provides the best performance when performing aspect-based sentiment analysis on text data from digital banking application reviews. The findings are expected to serve as a reference for the development of automated evaluation systems that rely on user opinions as the basis for decision making.
Keywords: aspects; CRISP-DM; digital Banking; seabank; sentiment analysis
Abstrak: Peningkatan pemakaian aplikasi perbankan digital mendorong perlunya pemahaman yang lebih dalam mengenai persepsi pengguna, terutama melalui analisis sentimen berbasis aspek. Penelitian ini bertujuan untuk mengklasifikasikan sentimen pengguna aplikasi SeaBank dengan berfokus pada empat aspek utama: kemudahan dipelajari (learnability), efisiensi penggunaan (efficiency), kendala atau kesalahan teknis (error), serta tingkat kepuasan (satisfaction). Data ulasan berjumlah 1.971 komentar dikumpulkan dari Google Play Store dan diberi label sentimen berdasarkan skor (rating) yang diberikan oleh pengguna. Pendekatan CRISP-DM berfungsi sebagai kerangka metodologis untuk penelitian ini, yang mencakup lima algoritma klasifikasi: Naïve Bayes, Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Tree, dan Random Forest. Hasil evaluasi menunjukkan bahwa algoritma SVM memberikan performa terbaik dengan nilai rata-rata dari ke empat aspek tertinggi yang mencapai accuracy sebesar 93.91%, Precision sebesar 91.16%, recall sebesar 97.96% dan F1-Measure sebesar 94.33%. Menurut temuan penelitian, algoritma Support Vector Machine (SVM) memberikan kinerja terbaik saat melakukan analisis sentimen berbasis aspek pada data teks dari ulasan aplikasi Seabank. Temuan ini diharapkan dapat menjadi referensi bagi pengembangan sistem evaluasi otomatis yang mengandalkan opini pengguna sebagai dasar pengambilan keputusan.
Kata kunci: Analisis Sentimen, Aspek, Bank Digital, SeaBank, CRISP-DM
Abstract:Abstract: The rapid growth of the cosmetics industry on e-commerce platforms has intensified competition, creating a critical need for effective, data-driven marketing strategies. This study aims to conduct a comparative…
analysis of machine learning algorithms to predict the sales categories (High, Medium, Low) of cosmetic products on the Tokopedia marketplace. Four classification models; Random Forest, XGBoost, Logistic Regression, and Naive Bayes were trained and evaluated on data collected via web scraping. The methodology incorporates the Synthetic Minority Over-sampling Technique (SMOTE) to address significant class imbalance and GridSearchCV for hyperparameter optimization to ensure a fair and robust comparison. The experimental results conclusively show that the Random Forest model achieved the best performance, yielding the highest F1-Score Macro Average of 0.75 and an accuracy of 85.3%. The superior model was subsequently implemented in a simple recommendation system to simulate optimal discount strategies, demonstrating its practical utility in providing actionable insights for business decisions.
Keywords: classification; comparative analysis; machine learning; sales prediction; SMOTE
Abstrak: Pertumbuhan pesat industri kosmetik pada platform e-commerce telah membuat persaingan ketat, sehingga menciptakan kebutuhan krusial akan strategi pemasaran yang efektif dan berbasis data. Penelitian ini bertujuan untuk melakukan analisis komparatif terhadap algoritma machine learning untuk memprediksi kategori penjualan (Tinggi, Sedang, Rendah) produk kosmetik di marketplace Tokopedia. Empat model klasifikasi, yaitu Random Forest, XGBoost, Regresi Logistik, dan Naive Bayes, dilatih dan dievaluasi menggunakan data yang dikumpulkan melalui web scraping. Metodologi penelitian ini menerapkan Synthetic Minority Over-sampling Technique (SMOTE) untuk mengatasi ketidakseimbangan kelas yang signifikan dan GridSearchCV untuk optimisasi hyperparameter guna memastikan perbandingan yang adil. Hasil eksperimen menunjukkan bahwa model Random Forest mencapai performa terbaik, dengan menghasilkan F1-Score Macro Average tertinggi sebesar 0,75 dan akurasi 85,3%. Model unggul ini kemudian diimplementasikan dalam sebuah sistem rekomendasi sederhana untuk menyimulasikan strategi diskon yang optimal, yang menunjukkan kegunaan praktisnya dalam memberikan wawasan yang dapat ditindaklanjuti untuk pengambilan keputusan bisnis.
Kata kunci: analisis komparatif; klasifikasi; machine learning; prediksi penjualan; SMOTE
Abstract:Abstract: Current technological developments play a crucial role in driving the tourism industry, one of which is the utilization of technology. However, tourist attractions in border areas face challenges such as limited…
d access to digital information and a lack of interactive media to present local wisdom such as history, customs, and culture. This study aims to develop and implement a QR Code-based Web-based Augmented Reality (WebAR) to digitize local wisdom at tourist attractions, making information more engaging and accessible to tourists. The research methodology adopted three approaches: UCD (User-Centered Design), Agile methods, and TAM (Technology Acceptance Model). This platform contains the local wisdom of two tourist villages in the border area, namely Sebujit Village and Jagoi Babang Village. The results of testing and evaluation using multiple linear regression and SEM-PLS methods on 45 respondents showed that the WebAR technology acceptance model was significant (F = 6.583; p < 0.001). User Attitude (ATU) is a key variable that significantly influences Intention to Use (BI) (β=0.429; p=0.001), while Ease of Use (PEOU) and Benefit (PU) indirectly influence BI through ATU. As additional validation, the classification test yielded an accuracy of 88.90% and an F1-score of 0.941, confirming that QR Code-based WebAR is effective and well-received as a digital information and promotion medium for local wisdom in border areas.
Keywords: border areas; local wisdom; qr code; tourist attractions; web augmented reality.
Abstrak: Perkembangan teknologi saat ini memiliki peran penting dalam mendorong industri pariwisata, salah satunya dengan pemanfaatan teknologi. Namun, objek wisata di daerah perbatasan menghadapi tantangan seperti keterbatasan akses informasi digital dan kurangnya media interaktif untuk menyajikan kearifan lokal seperti sejarah, adat istiadat, dan budaya. Penelitian ini bertujuan untuk mengembangkan dan mengimplementasikan Web-based Augmented Reality (WebAR) berbasis QR Code untuk digitalisasi kearifan lokal objek wisata, menjadikan informasi lebih menarik dan mudah diakses bagi wisatawan. Metodologi penelitian mengadopsi tiga pendekatan, UCD (User-Centered Design), metode Agile, dan TAM (Technology Acceptance Model). Platform ini memuat kearifan lokal dua desa wisata di daerah perbatasan, yaitu Desa Sebujit dan Desa Jagoi Babang. Hasil pengujian dan evaluasi dengan metode regresi linier berganda dan SEM-PLS pada 45 responden menunjukkan bahwa model penerimaan teknologi WebAR ini signifikan (F = 6.583; p < 0.001). Sikap Pengguna (ATU) menjadi variabel kunci yang berpengaruh signifikan terhadap Niat Penggunaan (BI) (β=0.429; p=0.001), sementara Kemudahan Penggunaan (PEOU) dan Manfaat (PU) memengaruhi BI secara tidak langsung melalui ATU. Sebagai validasi tambahan, uji klasifikasi menghasilkan akurasi 88,90% dan F1-score 0,941, yang menegaskan bahwa WebAR berbasis Kode QR efektif dan dapat diterima dengan baik sebagai media informasi dan promosi digital kearifan lokal di wilayah perbatasan.
Kata Kunci: daerah perbatasan; kearifan lokal; qr code; web augmented reality.
Abstract:Abstract: MBG is a strategic program of the Prabowo-Gibran administration. This program has become a widely discussed issue in the public. To better understand public perception of this program, sentiment analysis is necessary.…
essary. This study aims to compare the performance of algorithms machine learning SVM, RF, And BERT with preprocessing data analyzing public sentiment of the MBG program in media X. The total dataset for this study was 39,858 out of 42,465 successfully crawled tweets. The research methods included data collection, preprocessing data (cleaning, case folding, word normalization, stopword removal and stemming), feature extraction, model training (fine-tuning), handling class imbalance with SMOTE, and evaluation using accuracy, precision, recall, and f1-score. The research results show that without SMOTE, the best performing models are BERT with 89% accuracy, SVM 87%, and RF 78.4%. After SMOTE, the best algorithms were SVM with 92.94%, BERT with 88.3%, and RF with 86.59%. The results confirmed that SVM is the best algorithm if at leastclass imbalance. BERT is the best algorithm before and after SMOTE, because BERT is more effective in capturing the nuances of language on social media, so BERT is the most recommended in MBG sentiment analysis.
Keywords: sentiment analysis; machine learning; SVM, RF, and BERT
Abstrak: MBG merupakan program strategis pemerintahan Prabowo - Gibran. Program ini menjadi isu yang banyak diperbincangkan publik. Untuk mengetahui lebih dalam persepsi masyrakat tentang program ini, perlu dilakukan analisis sentiment. Penelitian ini bertujuan membandingkan kinerja algoritma machine learning SVM, RF, dan BERT dengan preprocessing data menganalisis sentiment public program MBG di media X. Total dataset penelitian ini adalah 39.858 dari 42.465 tweet yang berhasil di crawling. Metode penelitian mencakup pengumpulan data, preprocessing data (cleaning, case folding, normalisasi kata, stopword removal dan stemming), ekstraksi fitur, pelatihan model (fine-tuning), penanganan class imbalance dengan SMOTE, dan evaluasi menggunakan akurasi, presisi, recall, dan f1-score. Hasil peneltian menunjukkan, tanpa SMOTE model dengan kinerja terbaik adalah BERT dengan akurasi 89%, SVM 87%, dan RF 78,4%. Setelah SMOTE algoritma terbaik adalah SVM 92,94%, BERT 88,3% dan RF 86,59%. Hasil penelitian menegaskan bahwa SVM adalah algoritma terbaik jika minimal class imbalance. BERT adalah algoritma terbaik sebelum dan sesudah SMOTE, karena BERT lebih efektif dalam menangkap nuansa bahasa pada media sosial, sehingga BERT paling di rekomendasikan dalam analisis sentimen MBG.
Kata kunci: analisis sentimen; machine learning; SVM, RF, dan BERT
Abstract:Abstract: The development of digital learning systems requires not only effective content delivery but also database consistency and performance, particularly when used at scale by lecturers and students. Weaknesses in database…
atabase design can lead to data duplication, relational violations, and transaction failures that compromise system reliability. This study designed the Royal Mengajar application using PHP and MySQL, supported by JavaScript, HTML, and Bootstrap 5. The Crowdsourced Academic Content model enables lecturers to contribute learning materials openly, while students evaluate them through a user rating system. The objective of this research is to design and optimize the database architecture of the Royal Mengajar application by implementing multiple control mechanisms—namely views, triggers, transactions, and constraints—to enhance data efficiency, consistency, and integrity in digital learning environments. Database optimization focuses on the use of views to improve query efficiency, triggers to maintain automatic consistency, transactions to ensure atomicity in multi-table operations, and constraints to preserve data integrity. The results show that views reduced the average query execution time to 0.12 seconds, triggers maintained consistency without manual intervention, and constraints achieved 100% referential integrity. The application of these mechanisms significantly improved system speed, reduced data redundancy, and enhanced information reliability, thus reinforcing the sustainability of Royal Mengajar as a community-driven learning platform
Keywords: crowdsourced academic content; constraint; database optimization; trigger.
Abstrak: Pengembangan sistem pembelajaran digital tidak hanya menuntut penyajian materi, tetapi juga konsistensi serta kinerja basis data ketika sistem digunakan secara masif oleh dosen dan mahasiswa. Kelemahan rancangan database dapat menimbulkan duplikasi data, pelanggaran relasi, dan kegagalan transaksi yang memengaruhi keandalan sistem. Penelitian ini merancang aplikasi Royal Mengajar berbasis PHP dan MySQL dengan dukungan JavaScript, HTML, dan Bootstrap 5. Model Crowdsourced Academic Content memungkinkan dosen berkontribusi secara terbuka, sedangkan mahasiswa melakukan evaluasi melalui user rating system. Tujuan penelitian ini adalah untuk merancang dan mengoptimalkan basis data aplikasi Royal Mengajar melalui penerapan berbagai mekanisme pengendali, seperti view, trigger, transaction, dan constraint, guna meningkatkan efisiensi, konsistensi, dan integritas data dalam sistem pembelajaran digital. Optimalisasi database difokuskan pada penerapan view untuk efisiensi query, trigger untuk menjaga konsistensi otomatis, transaction untuk memastikan atomicity pada operasi multi-tabel, serta constraint guna menjamin integritas data. Hasil pengujian menunjukkan view menurunkan rata-rata waktu eksekusi query menjadi 0,12 detik, trigger menjaga konsistensi tanpa intervensi manual, dan constraint memastikan integritas referensial tercapai 100%. Penerapan mekanisme ini berdampak pada peningkatan kecepatan sistem, berkurangnya redundansi, serta keandalan informasi yang lebih tinggi, sehingga mendukung keberlanjutan Royal Mengajar sebagai platform pembelajaran berbasis kontribusi komunitas.
Kata kunci: basis data; optimasi; trigger; constraint; crowdsourced academic content.
Abstract:Abstract: Stroke is one of the leading causes of death and disability in various parts of the world, including in Indonesia. Along with the development of digital technology, the use of Machine Learning in the health sector…
tor is growing, one of which is in an effort to predict the occurrence of stroke. This study aims to implement the Logistic Regression algorithm in predicting the likelihood of a person having a stroke based on data from the Brain Stroke dataset. The research process includes data preprocessing (missing value handling, normalization, and label encoding), dividing the data into 80% training data and 20% test data, as well as model training. The model was then evaluated using several measures such as accuracy, precision, recall, F1-score, and ROC-AUC, as well as a confusion matrix. The results of the study showed that Logistic Regression was able to provide stroke classification results with an accuracy of 82.4%, precision of 80.1%, recall of 78.6%, F1-score of 79.3%, and a ROC-AUC value of 0.87. Then, the model is integrated into applications that use Streamlit, so it can be used interactively to predict stroke risk in new data. The results of this study show that the combination of Machine Learning and web-based applications has the potential to support efforts to detect early stroke risk.
Keywords: logistic regression; machine learning; prediction; streamlit; stroke.
Abstrak: Stroke adalah salah satu penyebab utama kematian dan kecacatan di berbagai belahan dunia, termasuk di Indonesia. Seiring perkembangan teknologi digital, penggunaan Machine Learning dalam bidang kesehatan semakin berkembang, salah satunya dalam upaya memprediksi terjadinya penyakit stroke. Penelitian ini bertujuan untuk mengimplementasikan algoritma Logistic Regression dalam memprediksi kemungkinan seseorang mengalami stroke berdasarkan data dari dataset Brain Stroke. Proses penelitian meliputi preprocessing data (penanganan missing value, normalisasi, dan label encoding), membagi data menjadi 80% data latih dan 20% data uji, serta pelatihan model. Model kemudian dievaluasi menggunakan beberapa ukuran seperti akurasi, precision, recall, F1-score, dan ROC-AUC, serta confusion matrix. Hasil penelitian menunjukkan bahwa Logistic Regression mampu memberikan hasil klasifikasi penyakit stroke dengan akurasi sebesar 82,4%, precision 80,1%, recall 78,6%, F1-score 79,3%, dan nilai ROC-AUC sebesar 0,87. Kemudian, model tersebut diintegrasikan ke dalam aplikasi yang menggunakan Streamlit, sehingga dapat digunakan secara interaktif untuk memprediksi risiko stroke pada data baru. Hasil penelitian ini menunjukkan bahwa kombinasi Machine Learning dan aplikasi berbasis web berpotensi mendukung upaya deteksi dini risiko stroke.
Kata kunci: logistic regression; machine learning; prediksi; streamlit; stroke.
Abstract:Abstract: Higher education plays an essential role in improving human resource quality, one of which is through the institution’s ability to monitor and predict student graduation outcomes. This study does not focus on a…
a specific university but utilizes the publicly available Students Performance in Exams dataset from Kaggle, consisting of 1,000 student records containing mathematics, reading, and writing scores, along with demographic attributes such as gender, parental education level, lunch type, and test preparation participation. The data were processed through a feature engineering stage by adding an average score variable as an early indicator of graduation status. A predictive model was developed using the Random Forest Classifier, achieving an accuracy of 94.5%. The final model was integrated into a Streamlit-based web application to provide an accessible tool for academic stakeholders. The results indicate that the proposed model can serve as an effective decision-support tool for early evaluation of students’ likelihood of graduation.
Keywords: prediction; random forest classifier, streamlit, student graduation.
Abstrak: Pendidikan tinggi memegang peran penting dalam peningkatan kualitas sumber daya manusia, salah satunya melalui kemampuan institusi dalam memantau dan memprediksi tingkat kelulusan mahasiswa. Penelitian ini tidak berfokus pada perguruan tinggi tertentu, melainkan menggunakan dataset publik Students Performance in Exams dari Kaggle yang berisi 1.000 data mahasiswa, terdiri atas nilai matematika, membaca, menulis, serta atribut demografis seperti gender, tingkat pendidikan orang tua, jenis makan siang, dan partisipasi kursus persiapan. Data diolah melalui tahap feature engineering dengan menambahkan variabel average score sebagai indikator awal kelulusan. Model prediksi dibangun menggunakan algoritma Random Forest Classifier, yang menghasilkan tingkat akurasi sebesar 94,5%. Model ini kemudian diimplementasikan ke dalam aplikasi web berbasis Streamlit untuk memberikan layanan prediksi yang mudah diakses oleh pihak akademik. Hasil penelitian menunjukkan bahwa model mampu digunakan sebagai alat pendukung keputusan untuk melakukan evaluasi dini terhadap potensi kelulusan mahasiswa.
Kata kunci: kelulusan mahasiswa; prediksi; random forest classifier; streamlit.
Abstract:Abstract: In the era of sensitive health data and frequent cyberattacks, securing electronic medical records (EMR) has become a critical challenge. This study proposes a hybrid encryption framework combining Affine and AES…
ES algorithms with an AI-based key management module to enhance EMR security while maintaining efficiency. A dataset of 1,000 simulated records was evaluated using five cryptographic configurations: Affine-only, AES-only, RSA-only, Affine–AES, and Affine–AES with AI. Performance was measured through encryption/decryption latency and ciphertext size, while security was assessed under brute-force, SQL injection, and phishing simulations. The AI decision tree for key generation was evaluated using accuracy, precision, recall, F1-score, and entropy metrics. Results show that the AI-enhanced hybrid method eliminates brute-force success, introduces only minor latency overhead, and generates high-entropy keys with reliability above 98%. These findings indicate that integrating AI-based dynamic key regeneration into hybrid encryption can improve EMR security while remaining practical for clinical and cloud-based healthcare systems. Future work should involve real clinical datasets and explore post-quantum cryptographic extensions.
Keywords: AI key management; attack resistance; encryption performance; electronic medical records; hybrid encryption
Abstrak: Di era meningkatnya sensitivitas data kesehatan dan maraknya serangan siber, perlindungan Rekam Medis Elektronik (RME) menjadi tantangan penting. Penelitian ini mengusulkan kerangka enkripsi hibrida yang menggabungkan algoritma Affine dan AES dengan modul manajemen kunci berbasis AI untuk meningkatkan keamanan RME tanpa mengorbankan efisiensi. Dataset simulasi berisi 1.000 entri diuji menggunakan lima konfigurasi kriptografi: Affine-only, AES-only, RSA-only, Affine–AES, serta Affine–AES dengan AI. Performa diukur melalui latensi enkripsi/dekripsi dan ukuran ciphertext, sedangkan keamanan dievaluasi melalui simulasi serangan brute force, SQL injection, dan phishing. Model decision tree untuk manajemen kunci dinilai menggunakan metrik akurasi, presisi, recall, F1-score, dan entropi. Hasil menunjukkan bahwa metode hibrida dengan AI menghilangkan keberhasilan brute force, menambah overhead latensi yang minimal, serta menghasilkan kunci berentropi tinggi dengan reliabilitas di atas 98%. Temuan ini menunjukkan bahwa regenerasi kunci dinamis berbasis AI dalam skema enkripsi hibrida dapat meningkatkan keamanan RME sekaligus tetap praktis untuk sistem klinis dan layanan kesehatan berbasis cloud. Penelitian selanjutnya disarankan menggunakan dataset klinis nyata dan mengeksplorasi kriptografi pascakuantum.
Kata kunci: enkripsi hibrida; ketahanan serangan; kinerja enkripsi; manajemen kunci berbasis AI; rekam medis elektronik
Abstract:Abstract: Recommendation systems are becoming increasingly important with the growth of streaming platforms. The purpose of this study is to compare the performance of Content-Based Filtering, Neural Collaborative Filtering,…
ing, and a combination of both in a movie recommendation system. The method used in this study involves retrieving movie details from the TMDB API and ratings from the MovieLens 32M Dataset (2010-2023). Each model's performance is evaluated using evaluation metrics such as RMSE and MAE. The results of this study indicate that Neural Collaborative Filtering achieves the best prediction performance (RMSE = 0.785423, MAE = 0.581262), followed by the hybrid model (RMSE = 0.800863, MAE = 0.660872), while Content-Based Filtering produces low performance and limits the capabilities of the hybrid model. In conclusion, these findings highlight the superiority of latent feature-based models such as NCF that learn directly from user interaction patterns over content-based approaches in the context of modern recommendation systems.
Keywords: content-based filtering; hybrid filtering; movie recommendation; neural collaborative filtering.
Abstrak: Sistem rekomendasi menjadi semakin penting seiring berkembangnya platform streaming. Tujuan dari penelitian ini adalah membandingkan kinerja Content-Based Filtering, Neural Collaborative Filtering dan kombinasi keduanya dalam sistem rekomendasi film. Metode yang digunakan dalam penelitian ini melibatkan pengambilan detail film dari TMDB API dan rating dari dataset MovieLens 32M Dataset (2010-2023). Setiap peforma model dievaluasi dengan menggunakan metrik evaluasi seperti RMSE dan MAE. Hasil dari penelitian ini menunjukkan bahwa Neural Collaborative Filtering mencapai kinerja prediksi terbaik (RMSE = 0.785423, MAE = 0.581262), diikuti oleh model hybrid (RMSE = 0.800863, MAE = 0.660872), sementara Content-Based Filtering menghasilkankan peforma yang rendah dan membatasi kemampuan model hybrid. Kesimpulannya, penelitian ini menyoroti superiotas model berbasis latent feature seperti NCF yang belajar langsung dari pola interaksi pengguna dibandingkan pendekatan berbasis konten dalam konteks sistem rekomendasi modern.
Kata kunci: content-based filtering; hybrid filtering; neural collaborative filtering; rekomendasi film.
Abstract:Abstract: The Ministry of Education, Culture, Research, and Technology, has developed guidelines for the Internal Quality Assurance System or known as SPMI, that is being implemented through the Internal Quality Audit (IQA)…
QA) with the PPEPP cycle, namely Determination (P), Implemen-tation (P), Evaluation (E), Control (P), and Improvement (P). Some universities have implemented IQA with system. The problem is that the system does not line well with the PPEPP cycle, which results in unsatisfactory audit results. The purpose of this study is to evaluate how well the university-owned AQI system features in line the PPEPP cycle and to highlight development opportunities. The method used Feature Oriented Domain analysis (FODA) and Acceptance Testing. This study delivered an analysis of IQA system features that consistent with PPEPP. The FODA results were validated by expert and tested with User Acceptance Test (UAT) with 89.98% user response that the system is acceptable. The research contributes to universities' understanding of the features necessary in the AQI system, which has an impact on the perfection of the university AQI system design in accordance with the PPEPP cycle.
Keywords: FODA; IQA system; PPEPP cycle; SPMI
Abstrak: Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi telah menyusun pedoman Sistem Penjaminan Mutu Internal atau yang dikenal dengan SPMI, yang diimplementasikan melalui Audit Mutu Internal (AMI) dengan siklus PPEPP, yaitu Penetapan (P), Pelaksanaan (P), Evaluasi (E), Pengendalian (P), dan Peningkatan (P). Beberapa perguruan tinggi telah mengimplementasikan AMI dengan sistem. Permasalahannya, sistem tersebut tidak sejalan dengan siklus PPEPP, sehingga hasil audit kurang memuaskan. Tujuan dari penelitian ini adalah untuk mengevaluasi seberapa baik fitur sistem AMI milik perguruan tinggi sejalan dengan siklus PPEPP dan menyoroti peluang pengembangan. Metode yang digunakan adalah analisis Feature Oriented Domain (FODA) dan Acceptance Testing. Penelitian ini menghasilkan analisis fitur sistem AMI yang konsisten dengan PPEPP. Hasil FODA divalidasi oleh ahli dan diuji dengan User Acceptance Test (UAT) dengan 89,98% respon pengguna bahwa sistem dapat diterima. Penelitian ini memberikan kontribusi terhadap pemahaman universitas terhadap fitur-fitur yang diperlukan dalam sistem AMI, yang berdampak pada kesempurnaan desain sistem AMI universitas sesuai dengan siklus PPEPP.
Kata kunci: FODA; siklus PPEPP; sistem AMI; SPMI