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All Journal InComTech: Jurnal Telekomunikasi dan Komputer METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi JURIKOM (Jurnal Riset Komputer) Simtek : Jurnal Sistem Informasi dan Teknik Komputer STRING (Satuan Tulisan Riset dan Inovasi Teknologi) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Tecnoscienza JATI (Jurnal Mahasiswa Teknik Informatika) Infotek : Jurnal Informatika dan Teknologi SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) International Journal Software Engineering and Computer Science (IJSECS) Jurnal Teknik Informatika Duta.com : Jurnal Ilmiah Teknologi Informasi dan Komunikasi Proceeding of International Conference Health, Science And Technology (ICOHETECH) Prosiding Seminar Nasional Teknologi Informasi dan Bisnis JURNAL ILMIAH SAINS TEKNOLOGI DAN INFORMASI SmartComp Jurnal Pengabdian Masyarakat Inovasi Indonesia Journal of Information Technology Jurnal Publikasi Sistem Informasi dan Manajemen Bisnis
Aprilisa Arum Sari
Universitas Duta Bangsa Surakarta
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

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Pendekatan Interpretable Machine Learning untuk Analisis Keberhasilan Kampanye Pemasaran Menggunakan CatBoost dan SHAP Aprilisa Arum Sari; Oktalia Kumala Sari
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 2 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i2.34715

Abstract

Predicting the success of digital marketing campaigns remains a significant challenge due to the complex interactions among various variables, such as budget allocation and advertising channel selection. This study aims to develop a marketing analytics model that achieves high predictive accuracy while also providing clear interpretability of the prediction results. The study uses the SalesMind Marketing Campaigns 2026 dataset, which simulates 3,478 digital marketing campaign records from 2026. The dataset consists of categorical variables such as ad_channel and campaign_type, as well as numerical variables including marketing_spend, impressions, and conversion_rate as the prediction target. The proposed approach applies Interpretable Machine Learning by combining the CatBoost algorithm to predict conversion rates and SHAP (SHapley Additive exPlanations) to analyze the contribution of each variable. Model optimization was performed using GridSearchCV, resulting in excellent performance with an RMSE of 0.0012, an MAE of 0.005, and a coefficient of determination (R²) of 99.12%. The analysis results indicate that budget allocation is the most dominant factor in improving conversion rates without showing indications of diminishing marginal effectiveness. In addition, the use of interactive platforms such as Meta and TikTok significantly contributes to campaign effectiveness. These findings contribute to providing an accurate and informative predictive model that can support strategic decision-making in digital marketing management more effectively.
Rancang Bangun Sistem Informasi Digitalisasi UMKM Kuliner Berbasis Web sebagai Media Promosi dan Pengelolaan Usaha Fathin Ryfsa Fadilah; Eko Purwanto; Aprilisa Arum Sari
Infotek: Jurnal Informatika dan Teknologi Vol. 9 No. 2 (2026): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v9i2.34998

Abstract

This research aims to design and develop a web-based information system for culinary MSME merchants to support the digitalization of promotion and business information management in a more effective and integrated manner. The existing problem is that data management and culinary information dissemination are still conducted manually, causing information related to shelters, food and beverage menus, and merchant data to be delivered less optimally to the public. The system development method used in this research is the Waterfall method, which consists of requirement analysis, system design, implementation, testing, and maintenance stages. The system was developed using the Laravel framework and MySQL database, while Unified Modeling Language (UML) was utilized as a system modeling tool. The system provides three access levels, namely admin, seller, and buyer, to support MSME data management, menu management, and centralized culinary information services. This study used 10 shelters as implementation testing data from approximately 120 available shelters. The testing results indicate that the system functions properly and assists users in accessing culinary information more quickly, effectively, and efficiently. The developed web-based system is expected to enhance culinary MSME promotion and support digital transformation in culinary business management.
MODEL PREDIKSI OMZET PENJUALAN MENGGUNAKAN RANDOM FOREST REGRESSION PADA UMKM TOKO KEMBAR Umi Fidya Hamidah; Aprilisa Arum Sari; Ratna Puspita Indah
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 10 No. 3 (2026): JATI Vol. 10 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v10i3.18888

Abstract

UMKM retail di Indonesia, termasuk Toko Kembar di Sukoharjo, masih mengandalkan pencatatan transaksi secara manual sehingga data penjualan belum dimanfaatkan secara optimal. Permasalahan utama yang dihadapi adalah ketidakmampuan memprediksi fluktuasi omzet penjualan, yang berdampak pada pengelolaan stok dan perencanaan keuangan yang reaktif. Penelitian ini bertujuan membangun model prediksi omzet penjualan berbasis Random Forest Regression untuk mendukung pengelolaan stok yang lebih terencana dan berbasis data. Metode yang digunakan adalah kerangka CRISP-DM dengan pendekatan panel data per produk per bulan, menggunakan rekayasa fitur berupa lag omzet 1–3 bulan, rata-rata bergerak 3 bulan, harga satuan, dan jumlah transaksi. Dataset mencakup 23.466 transaksi dari 8.720 produk selama periode September 2025–Maret 2026. Evaluasi model menggunakan MAE, RMSE, R², dan MAPE. Hasil evaluasi model menunjukkan nilai MAE sebesar Rp 32.579, RMSE sebesar 277.705, R² sebesar 0,6702 (67,02%), dan MAPE sebesar 8,45%. Prediksi omzet April 2026 sebesar Rp 661.396.513 dan Mei 2026 sebesar Rp 651.661.400 dengan rekomendasi penyesuaian stok pada beberapa produk.
SISTEM INFORMASI CUSTOMER RELATIONSHIP MANAGEMENT (CRM) UNTUK PENINGKATAN LOYALITAS PELANGGAN DI CAHAYA MANDIRI AUDIO & ELEKTRONIK Wildan Setiawan; Aprilisa Arum Sari; Dwi Hartanti
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 1 (2026): Volume 12 Nomor 1 Tahun 2026
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v12i1.5486

Abstract

Cahaya Mandiri Audio, an electronic service provider, is currently facing operational challenges due to its reliance on a manual record-keeping system, which has resulted in a data error rate of 15% and difficulties in maintaining customer loyalty. This study aims to design and build a web-based Customer Relationship Management (CRM) system capable of integrating customer data management, service history, and technician scheduling to optimize operations and improve customer retention. The development method used is the Prototype approach, utilizing the PHP programming language and the Laravel framework. The results of the study indicate that this system successfully unifies the roles of Admin and Technician within a single integrated platform, thereby minimizing human error and creating a more organized workflow compared to the previous manual system. The contribution and novelty of this system lie in the Scheduled Notification Module via WhatsApp, which facilitates the semi-automatic creation of message drafts through a redirect feature based on unit service history data to efficiently enhance customer loyalty.Keyword: CRM, Service, Laravel, Customer Loyalty, WhatsApp API.
RANCANG BANGUN SISTEM INFORMASI PENDATAAN ASET SEKOLAH DASAR BERBASIS WEB DENGAN QR CODE PADA DINAS PENDIDIKAN KOTA SURAKARTA MENGGUNAKAN METODE R&D Nabilla Putri Sari Fatikhah; Aprilisa Arum Sari; Agustina Purwatiningsih
Simtek : jurnal sistem informasi dan teknik komputer Vol. 11 No. 1 (2026): April 2026
Publisher : STMIK Catur Sakti Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51876/simtek.v11i1.1762

Abstract

The process of recording elementary school assets at the Surakarta City Education Office remains conventional, making it prone to errors in data entry, reporting delays, difficulties in data compilation, and inefficient asset inspections. This study was conducted to develop a web-based asset data management system integrated with QR codes to facilitate the identification, recording, and reporting of assets. The system is designed so that each asset has a unique code that can be scanned to display information quickly and accurately, while schools can upload asset data along with proof of purchase remotely. The results of the study show that the developed system supports centralized data collection, speeds up reporting, facilitates asset verification, and organizes documents more effectively. Thus, the system improves the performance, accuracy, and transparency of asset management in elementary schools.
Rancang Bangun Sistem Informasi Penjualan dan Stok Air Galon Berbasis Website pada Usaha Depot Air Isi Ulang Cemani Andreas Obrien Saragih; Marta Ardiyanto; Aprilisa Arum Sari
JURNAL ILMIAH SAINS TEKNOLOGI DAN INFORMASI Vol. 4 No. 3 (2026): Juli : Jurnal Ilmiah Sains Teknologi dan Informasi
Publisher : CV. ALIM'SPUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59024/jiti.v4i3.2288

Abstract

This research aims to design and build a website-based information system for gallon water sales and stock at the Cemani Refill Water Depot. The current problem is that the sales and stock recording process is still done manually, resulting in frequent recording errors, delays in reporting, and difficulties in monitoring inventory. This condition results in less effective and efficient operational processes. The system development method used in this research is a prototype method, which includes requirements gathering, prototype creation, prototype evaluation, system development, system testing, system evaluation, and system use. The system design was carried out using Unified Modeling Language (UML), while system testing utilized Black Box Testing.The result of this research is a web-based information system capable of managing sales data, gallon water stock data, customer data, and generating reports quickly and accurately. Based on the results of testing using the Black Box Testing method, all system features, such as login, customer data management, stock management, sales transactions, and report generation, functioned as expected without error. This system is expected to improve work efficiency, minimize recording errors, and facilitate business owners in monitoring business progress in real time.
Sistem Pendukung Keputusan Penentuan Kandidat Juara Piala Dunia FIFA 2026 Menggunakan Metode TOPSIS Khoiruddin Subhi; Fattah Alfarisi Wiryahadi; David Fajar Satriatama; Adimas Hengki Kurniawan; Aprilisa Arum Sari
Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Prosiding Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB) 2026
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/p4hpys86

Abstract

Sistem Pendukung Keputusan (SPK) untuk menentukan kandidat juara Piala Dunia FIFA 2026 dikembangkan menggunakan metode Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Metode TOPSIS digunakan karena mampu mendukung pengambilan keputusan multikriteria secara objektif melalui pengukuran kedekatan alternatif terhadap solusi ideal positif dan jaraknya dari solusi ideal negatif. Penelitian ini menggunakan lima kriteria penilaian, yaitu peringkat FIFA pra-turnamen, nilai pasar skuad, jumlah gol, jumlah kebobolan dalam empat tahun terakhir, serta status sebagai tuan rumah. Data penelitian diperoleh dari 48 negara peserta Piala Dunia FIFA 2026. Proses analisis dilakukan melalui tahapan normalisasi matriks keputusan, pembobotan kriteria, penentuan solusi ideal positif dan negatif, serta perhitungan nilai preferensi untuk menghasilkan perangkingan alternatif. Hasil penelitian menunjukkan bahwa Inggris memperoleh nilai preferensi tertinggi sebesar 0,689, diikuti oleh Prancis sebesar 0,685 dan Spanyol sebesar 0,671. Hasil tersebut menunjukkan bahwa metode TOPSIS mampu menghasilkan rekomendasi kandidat juara secara sistematis, objektif, dan transparan sehingga dapat digunakan sebagai pendukung pengambilan keputusan dalam menentukan kandidat juara Piala Dunia FIFA 2026.
Komparasi Algoritma Prediksi Churn Pelanggan Berbasis Machine Learning Pada Aplikasi Billing Admin Nur Afrianto; Aprilisa Arum Sari; Margaretha Evi Yuliana
Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Prosiding Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB) 2026
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

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

Abstract

Pada perusahaan yang menerapkan model bisnis berbasis langganan, perpindahan pelanggan ke kompetitor menjadi permasalahan penting karena berdampak langsung terhadap stabilitas pendapatan dan keberlangsungan hubungan pelanggan. Penelitian ini bertujuan untuk membandingkan performa algoritma LightGBM dan Decision Tree dalam memprediksi churn pelanggan serta mengimplementasikan model terbaik ke dalam aplikasi Billing Admin. Pelaksanaan penelitian mengacu pada metodologi CRISP-DM yang terdiri atas tahap business understanding, data understanding, data preparation, modeling, evaluation, dan deployment. Dataset customer churn yang digunakan terdiri dari 2.800 data pelanggan dan dievaluasi menggunakan metrik Accuracy, Precision, Recall, dan F1-Score. Jika dibandingkan dengan Decision Tree, algoritma LightGBM terbukti menghasilkan performa yang lebih superior. Hal ini ditunjukkan oleh perolehan Accuracy 0,6274, Recall 0,6819, dan F1-Score 0,6770. Model LightGBM kemudian berhasil diintegrasikan ke dalam aplikasi Billing Admin berbasis CodeIgniter 4 menggunakan Flask API untuk melakukan prediksi churn pelanggan secara otomatis. Dengan demikian, model yang dihasilkan mampu membantu perusahaan dalam mengidentifikasi pelanggan yang berpotensi churn dan mendukung pengambilan keputusan dalam strategi retensi pelanggan.
IMPLEMENTASI ALGORITMA CATBOOST UNTUK KLASIFIKASI KELOMPOK PENDAPATAN NEGARA (INCOME GRUP) BERDASARKAN INDIKATOR MALNUTRISI GLOBAL Vivi Apriya Mardani; Aprilisa Arum Sari; Ridwan Dwi Irawan
Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Prosiding Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB) 2026
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

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

Abstract

Masalah malnutrisi masih menjadi salah satu tantangan global yang berdampak terhadap kualitas sumber daya manusia dan pembangunan ekonomi suatu negara. Berbagai indicator melnutrisi, seperti stunting, wasting, underweight, overweight, undernourisment, dan food insecurity, memiliki keterkaitan dengan kondisi sosial ekonomi dan tingkat pendapatan suatu negara. Penelitian ini bertujuan untuk mengimplementasikan algoritma CatBoost dalam mengklaifikasikan kelompok pendapatan negara (income group) berdasarkan indicator malnutrisi global. Data penelitian diperoleh dari World Health Organization (WHO) dan World Bank yang terdiri atas empat ketegori kelompok pendapatan, yaitu Low Income, Lower-Middle-Income, Upper-Middle-Income, dan High Income. Metode penelitian menggunakan pendekatan machine learning dengan pembagian data sebesar 70% sebagai data pelatihan dan 30% sebagai data pengujian. Model dibangun menggunakan algoritma CatBoost dengan parameter iteration sebesar 100, depth sebesar 4, dan learning rate sebesar 0,05. Hasil penelitian menunjukkan bahwa model CatBoost memperoleh tingkat akurasi sebesar 93,33%. Selain itu, hasil classification rate menunjukkan nilai precision, racell, dan f1-score yang tinggi pada Sebagian besar kelas. Analisis feature importance menunjukkan bahwa indicator food insecurity moderate severe dan wastinhg merupakan variabel yang paling berpengaruh dalam menentukan kelompok pendapatan negara. Dengan demikian, algoritma CatBoost mampu mengklasifikasikan kelompok pendapatan negara berdasarkan indicator malutrisi global secara efektif dan akurat.
ANALISIS PEMILIHAN MARKETPLACE BERDASARKAN PREFERENSI KONSUMEN GENERASI Z MENGGUNAKAN METODE AHP DAN TOPSIS Rizky Betha Nur Ramadhany; Dhiya’U Dzihni Salim; Trastoto Adi Baskoro; Aprilisa Arum Sari
Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Prosiding Seminar Nasional Teknologi Informasi dan Bisnis (SENATIB) 2026
Publisher : Fakultas Ilmu Komputer Universitas Duta Bangsa Surakarta

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

Abstract

Pertumbuhan pesat e-commerce di Indonesia meningkatkan kompleksitas keputusan konsumen dalam memilih marketplace, khususnya pada Generasi Z yang memiliki karakteristik adaptif terhadap teknologi dan dipengaruhi oleh berbagai faktor dalam bertransaksi. Penelitian ini bertujuan menganalisis pemilihan marketplace berdasarkan preferensi konsumen Generasi Z menggunakan kombinasi metode Analytical Hierarchy Process (AHP) dan Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Data diperoleh melalui penyebaran kuesioner kepada 50 responden untuk pembobotan AHP dan 62 responden untuk penilaian alternatif pada TOPSIS. Pada tahap AHP dilakukan proses pembersihan data dan pengujian Consistency Ratiomarketplace berdasarkan penilaian responden yang memenuhi persyaratan konsistensi metode AHP. Meskipun demikian, hasil pembobotan belum dapat digeneralisasikan untuk merepresentasikan seluruh populasi Generasi Z sehingga penelitian selanjutnya disarankan memperluas jumlah responden yang memenuhi uji konsistensi serta melakukan analisis sensitivitas.
Co-Authors 'Aziiz Alfarobi, Muhammad Ilham Abim Febri Hananto Adimas Hengki Kurniawan Aditya Wadanur Agustina Purwatiningsih Agustina Purwatiningsih Amirah Munif, Jihan Andreas Obrien Saragih Anugrah S, Bintang Argathama Dewa Firmansyah Arkanatha Yoen Restu Zain, Arga Arya Rahardiansyah, Rifki Aurel Ardhelia Ayu Aprillia Baehaqi Bagus Saputra, Andika Cagiva Gilang Mahendra Yulianto Catur Pamungkas, Affandi Danang Saputra, Danang David Fajar Satriatama Dhiya’U Dzihni Salim Dwi Ahwati, Ringgyanita Dwi Hartanti Dwi Hartanti Dwi Hartanti Dwi Wahyuni, Tesa Ega Muhammad Atsir Eko Purwanto Erina Nurjihan Fathin Ryfsa Fadilah Fattah Alfarisi Wiryahadi FAULINDA ELY NASTITI Fauzi Rakhmadianto, Ridlo Fendy Adji Febriansyah Firdausi, Annisaa' Alam Hana Aulia Zahro Hasim Azari Hasna Khaira Aswha, Alisa Hasna Nafisa Az Zahro Helfian, Ongky Hendra Wardana, Aliefian Ibnu Is’ad, Fadhil Irawan, Ridwan Dwi Irvan Satrio Herlambang Jose A.Da.C.Monteiro Kevien Ollyvie Jolanda Khoiril Abidin, Muhammad Khoiruddin Subhi Kumala Sari, Oktalia Malika Endiwan, Aprillio Mandiri, Puput Dwi Margaretha Evi Yuliana Marta Ardiyanto Maula Rif’at, Nafi Miza, Kurniadi Muchammad Rosyid Aridho, Sayid Muhammad Dhafa Diar Ardhana Muhammad Fatah Perdana Nabilla Putri Sari Fatikhah Nadin Denta Yasika Naufal Alif Abib Abidi Nazeda Queena Zahra Aryanto Nur Afrianto Nur Eka Febrianto, Hermawan Nur Farida, Azzah Nur Sofiyanto, Irfan Nurmalitasari Nurmalitasari Oktalia Kumala Sari Permatasari, Hanifah Prakoso, Aprilrianto Dirhamdan Pramono Pramono Pramono Prayoga Yudhi Putra, Latief Prima Ertansyah, Gilang Putra Pratama, Akmal Putri Setiawan, Amanda Rahardiyan Purnama, Brahmadita Raka Pramuja Ananda, Syaelan Ramadhan Oktaviano, Alberthino Ramadhani, Rafli Ratna Puspita Indah Renjo Passha Da Silva Marcal Ridwan Fiki Setiawan Risky Kurniawan, Habib Risky, Muhammad Rivaldi Faqih Rahmawan Riyadi, Bagus Rizky Betha Nur Ramadhany Romadona, Romadona Sabda Rahino Jati Saputra, Bintang Pratama Yuarna Saputra, Ilham Trisatdika Sebrina Rizky Ardania Sekar, Lola Sely Evanda Setyawan, Yanuar Anggit Sharla Surya Rifa Silma Farikha Siti Salma Sholihatul Munawaroh SRI SUMARLINDA Sulanjari, Titi Tariq, Tariq Trastoto Adi Baskoro Tri Cahya Kusuma, Ridwan Trisna Aqilla, Fiza Triyono Triyono Ulhaq, Ahmad Dia Umi Fidya Hamidah Vihi Atina Vivi Apriya Mardani Vrisco Seno Aji Wakhid Nur Sakti, Didan Wildan Setiawan Wupiwulang Tanwal Hu Yudha P, Rizal