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All Journal Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal Teknik Komputer AMIK BSI JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Sisfokom (Sistem Informasi dan Komputer) JSiI (Jurnal Sistem Informasi) Krea-TIF: Jurnal Teknik Informatika IJISTECH (International Journal Of Information System & Technology) Jurnal ICT : Information Communication & Technology Health Sciences and Pharmacy Journal Building of Informatics, Technology and Science Madani : Indonesian Journal of Civil Society Jurnal Pengabdian kepada Masyarakat Nusantara Teknika IJISTECH Journal of Soft Computing Exploration Jurnal Pengabdian Masyarakat : Pemberdayaan, Inovasi dan Perubahan Batara Wisnu : Indonesian Journal of Community Services The Indonesian Journal of Computer Science Research SmartComp The Indonesian Journal of Computer Science
Acihmah Sidauruk
Universitas AMIKOM Yogyakarta
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Comparative study of marker-based and markerless tracking in augmented reality under variable environmental conditions Sulistiyono, Mulia; Hasyim, Jaka Wardana; Bernadhed, Bernadhed; Liantoni, Febri; Sidauruk, Acihmah
Journal of Soft Computing Exploration Vol. 5 No. 4 (2024): December 2024
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v5i4.503

Abstract

Augmented reality (AR) technology integrates virtual content into real environments using two main methods: marker-based and markerless tracking. Marker-based tracking relies on printed markers for object placement, while markerless uses environmental features for flexibility and accuracy. This research aims to evaluate the combined impact of environmental factors-distance, angle, and lighting-on these two methods. The Multimedia Development Life Cycle (MDLC) methodology was applied by testing 72 combinations of indicators: distance (5-120 cm), angle (30°, 45°, 90°), and light color (red, blue, green, yellow) using Xiaomi Note 8 and Google Pixel 4. Results show markerless tracking is superior in all conditions, achieving a 94.4% success rate on both devices. In contrast, marker-based tracking only achieved 72.2% (Xiaomi Note 8) and 77.8% (Google Pixel 4). Markerless tracking was optimally performed from 50 cm away and up close, while marker-based tracking degraded in performance at long distances and red lighting. Markerless tracking proved to be more reliable and consistent, suitable for dynamic and diverse environments, while marker-based methods remained relevant for short distances and controlled lighting. These findings provide guidance for AR developers in choosing a tracking methodology according to application needs.
The Effect of SMOTE and Optuna Hyperparameter Optimization on TabNet Performance for Heart Disease Classification Wijayanto, Danang; Marco, Robert; Sidauruk, Acihmah; Sulistiyono, Mulia
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 2 (2025): MEY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i2.2348

Abstract

Heart disease is a medical condition affecting the cardiovascular system, disrupting blood circulation and reducing cardiac function efficiency, which can lead to severe health complications. Early diagnosis of heart disease has become increasingly crucial as delayed detection can significantly impact patient outcomes and survival rates. While numerous studies have explored various approaches for heart disease classification, challenges related to data imbalance and improper parameter settings remain persistent issues that affect model performance. This research evaluated the effectiveness of combining TabNet with SMOTE and optuna hyperparameter optimization for heart disease classification. We conducted four experimental scenarios using a heart disease dataset with 303 instances: baseline TabNet, baseline TabNet with SMOTE, TabNet with Optuna, and TabNet with both SMOTE and Optuna. Results demonstrated that applying SMOTE alone to TabNet decreased model performance (accuracy from 85.24% to 77.04%, AUC from 0.89 to 0.83). However, when combining SMOTE with Optuna hyperparameter optimization, we achieved optimal performance with 90.16% accuracy, 93.33% precision, 87.50% recall, 90.32% F1-score, and 0.93 AUC. This represented a significant improvement over other configurations and several previous classification approaches. The integration of SMOTE with Optuna optimization  provided an effective framework for heart disease classification that outperformed traditional methods particularly in discriminative capability as evidenced by the superior AUC score.
Analisis Perbandingan Ekstraksi Fitur Teks pada Sentimen Analisis Kenaikan Harga BBM Darmawan, Briga; Laksito, Arif Dwi; Yudianto, Muhammad Resa Arif; Sidauruk, Acihmah
Krea-TIF: Jurnal Teknik Informatika Vol 11 No 1 (2023)
Publisher : Fakultas Teknik dan Sains, Universitas Ibn Khaldun Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32832/krea-tif.v11i1.13819

Abstract

BBM merupakan bahan bakar yang digunakan kendaraan bermotor. Penggunaan BBM meningkat sejalan dengan pertumbuhan ekonomi di Indonesia. Kenaikan harga BBM di Indonesia menimbulkan berbagai macam pendapat di media sosial twitter melalui posting dan thread. Fokus penelitian ini melakukan analisis sentimen terhadap kenaikan BBM yang datanya didapat melalui twitter dengan jumlah 1667 data. Tujuan dari penelitian ini melakukan perbandingan metode ekstraksi fitur yang memiliki kinerja paling baik seperti TF-IDF, Bag of Word, dan FastText diuji dengan algoritma machine learning SVM. Untuk tahap penelitian yang pertama melakukan crawling data twitter, preprocessing data, ekstraksi fitur, pembuatan model dengan algoritma machine learning, dan kemudian dilakukan pengujian dan perbandingan model confusion matrix pada setiap ekstraksi fitur. Hasil dari penelitian ini menunjukkan bahwa penggunaan ekstraksi fitur BoW  memiliki kinerja lebih baik dibandingkan model ekstraksi fitur yang lain.
The Analisis Perancangan Grafis Lingkungan Wisata untuk Membangkitkan Desa Wisata Ledhok Blotan Rahayu, Dwi; Sidauruk, Acihmah; Waskito, Bimo; Mulyatun, Sri
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 6 No. 1 (2025): Jurnal Pengabdian kepada Masyarakat Nusantara Edisi Januari - Maret
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v6i1.4832

Abstract

Desa wisata merupakan salah satu sektor pariwisata daerah yang berdampak langsung terhadap kesejahteraan masyarakat setempat. Desa wisata berada dibawah naungan dinas pariwisata namun dikelola oleh kelompok masyarakat. Di Daerah Istimewa Yogyakarta terdapat 214 desa wisata, salah satu diantaranya Ledhok Blotan yang berada di kabupaten Sleman. Pesona alam yang masih terjaga dan aliran sungai dari lereng gunung merapi yang tenang, menjadi daya tarik tempat wisata ini. Sebelum pandemi Covid-19 desa wisata Ledhok Blotan ini tidak pernah sepi pengunjung, bahkan sempat viral di sosial media. Akibat pandemi, Ledhok Blotan mendadak menjadi sepi sehingga melemahkan perekonomian warga setempat dan karena beberapa anggota pengelola inti meninggal dunia akibat covid-19, maka pada saat pasca pandemi desa wisata ini sangat kesulitan untuk bangkit. Adapun bantuan dari dinas pariwisata, namun anggota pengelola sudah tidak lagi mau mengelola. Saat ini, diluar naungan dinas pariwisata, ketua RT yang juga merupakan salah satu pemilik lahan lokasi wisata, tengah membentuk tim pengelola baru untuk membangkitkan desa wisata ini. Seluas 50% dari area wisata ini sudah kembali mulai dikunjungi wisatawan. 50 % area lainnya masih ngangkrak karena keterbatasan tenaga yang merawatnya. Melalui pengabdian masyarakat ini kami mendukung kebangkitan desa wisata Ledhok Blotan melalui pengadaan properti Sign Board dilingkungan wisata. Signboard sebelumnya telah rusak dan tidak lengkap. Pamor di sosial media cukup baik, namun tidak sepadan ketika berkunjung ke lokasi secara langsung. Oleh karenanya kebutuhan signboard, landmark, dan pictogram sangat dibutuhkan untuk menghidupkan kembali desa wisata ini. Masyarakat setempat masih berharap desa wisata ini kembali menjadi sumber perekonomian.
Pemanfaatan Teknologi Generatif Artificial Intelligence sebagai Media Promosi bagi Guru dan Staf Publikasi di Sekolah Dasar Satria, Dhimas Adi; Sidauruk, Acihmah; Sutarni, Sutarni; Pirmansah, Imam Ainudin
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 6 No. 3 (2025): Edisi Juli - September
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v6i3.6211

Abstract

Perkembangan Kecerdasan Buatan (Artificial Intelligence/AI) telah menyentuh berbagai aspek kehidupan manusia, termasuk dunia pendidikan. Salah satu bentuk inovasi AI yang memiliki potensi besar adalah Generatif Artificial Intelligence (Gen-AI), sebuah teknologi yang mampu menghasilkan konten multimedia seperti teks, gambar, video, dan audio berdasarkan data yang dilatihkan. Teknologi ini dapat dimanfaatkan oleh guru, siswa, dan staf sekolah untuk mendukung proses pembelajaran, promosi sekolah, serta dokumentasi kegiatan akademik. Namun, masih banyak tenaga pendidik dan staf di tingkat sekolah dasar yang belum memahami cara memanfaatkan Gen-AI secara optimal. Kegiatan pengabdian masyarakat ini bertujuan untuk memperkenalkan berbagai platform Gen-AI kepada guru dan staf sekolah dasar di Kota Yogyakarta, sekaligus memberikan pelatihan praktis dalam penggunaannya untuk menciptakan konten pendidikan dan promosi berbasis AI. Metode pelaksanaan terdiri dari tiga tahap utama: (1) analisis kebutuhan melalui survei dan wawancara untuk mengidentifikasi tantangan dan peluang penerapan Gen-AI di sekolah, (2) pendampingan langsung dalam penggunaan tools Gen-AI serta (3) pembuatan produk konten seperti video promosi sekolah dan dokumentasi kegiatan yang dapat digunakan untuk menyambut tahun ajaran baru. Hasil kegiatan menunjukkan bahwa Gen-AI dapat meningkatkan efisiensi dan kreativitas dalam pembuatan materi pembelajaran maupun promosi sekolah. Meskipun demikian, diperlukan pendampingan berkelanjutan untuk memastikan pemanfaatan teknologi ini tetap sesuai dengan kebutuhan pendidikan dan etika digital. Melalui pengabdian ini, diharapkan guru dan staf sekolah dapat lebih percaya diri dalam mengadopsi teknologi Gen-AI sebagai alat pendukung inovasi di lingkungan sekolah.
Comparative Analysis of Weighted-KNN, Random Forest, and Support Vector Machine Models for Beef and Pork Image Classification Using Machine Learning Satria, Budy; Afrianto, Nurdi; Ningsih, Lidya; Sakinah, Putri; Sidauruk, Acihmah; Mayola, Liga
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3736

Abstract

The actual problem that occurs in the sale of meat by some conventional market traders is mixing beef with pork because of the high selling price. The difference between pork and beef lies in the color and texture of the meat. However, many people do not understand this difference. This study aims to provide a solution to distinguish the two types of beef through a classification process by obtaining the best accuracy using the W-KNN, RF, and SVM models based on machine learning. This study compares the model's performance based on the number of datasets, comprising 400 original images (200 beef and 200 pork images), using a 80:20 ratio for training and test data. The extraction process uses two algorithms: HSV (Hue, Saturation, Value) and RGB (Red, Green, Blue). The model evaluation uses a confusion matrix that includes accuracy, Precision, Recall, and F1-score. Based on the results of the model testing, it was found that the random forest algorithm gave the best overall results, with the highest accuracy of 98.75%, Precision of 97%, F1-score of 98%, and recall of 99% on the number of decision trees of 400. This shows the stability and generalization of the superior model. The random forest algorithm is the most effective for classifying beef and pork data with minimal errors. Implications for further research include using a deep learning approach, especially for image processing, to detect differences in each meat characteristic and increase accuracy.
Object Recognition with SSD MobileNet Pre-Trained Model in the Cashier Application Burhanudin, Nazil Ilham; Laksito, Arif Dwi; Sidauruk, Acihmah; Yudianto, Muhammad Resa Arif; Rahmi, Alfie Nur
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 2 (2023): JULI
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i2.1659

Abstract

Object recognition is a type of image processing technique that is frequently employed in current applications such as facial identification, vehicle detection, and automated cashiers. One issue with barcode and RFID cashier apps is that they cannot scan several products at the same time. The cashier application employing object identification using picture images is believed to be able to distinguish more than one object in order to speed up the transaction process. The usage of SSD pre-trained models with MobileNet architecture to detect items in automatic cashier applications is discussed in this paper. This study put the model to the test on three types of soft drink objects: coca-cola, floridina, and good day. A smartphone camera was used to collect the data, which totaled 203 images. The findings indicated that the product object identification method was 82.9% accurate, 97.5% precise, and 84.7% recall. The object recognition process takes between 365 and 827 milliseconds, with an average time of 695 milliseconds (0.69 seconds).
Penerapan Composite Performance Index (CPI) Sebagai Metode Pada Sistem Pendukung Keputusan Seleksi Penerima Beasiswa Satria, Budy; Sidauruk, Acihmah; Wardhana, Raditya; Al Akbar, Abdussalam; Ihsan, M. Arinal
The Indonesian Journal of Computer Science Vol. 11 No. 2 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i2.3056

Abstract

The selection process for scholarship recipients in higher education requires a measurable system. The problem that has been happening is that the procedures carried out by the scholarship provider still use a manual file examination system. Composite performance index is the method used in this study. The purpose of this study is to create a decision support system for the selection of scholarship recipients to be more systematic and time efficient in the process. There are 10 alternatives and 4 criteria, namely parental income, GPA, electricity usage ,and semesters. The results of this study were obtained 5 highest values are A7 t with a value of 235.00 rank 1, A4 with a value of 200.00 rank 2, A1 with a value of 134.14 rank 3 sequences, A5 with a value of 120.00 ranks 4, and A8 with a value of 91.67 sequences 5.
Diagnosis Penyakit Tanaman Kopi Robusta Menggunakan Metode Dempster Shafer Berbasis Sistem Pakar Acihmah Sidauruk; Panggih Suseno; Budy Satria; Mulia Sulistiyono
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.3953

Abstract

Robusta coffee is a coffee variety that has unique characteristics, a strong taste and a different level of bitterness from Arabica coffee because Robusta coffee contains lower sugar and 2.2% more caffeine than Arabica coffee so that Robusta coffee production is quite helpful for the economy. several coffee producing countries in the world. The quality and productivity of coffee plants can decrease due to several factors such as pests and disease. However, the limitations of experts regarding coffee plant diseases are a factor and obstacle. The aim of this research is to create an expert-based intelligent system to identify pests and diseases in robusta coffee plants. The method that will be applied is Dempster Shafer. Data on disease names amounted to 13 and data on symptoms amounted to 27. The final result was that Robusta coffee plants were tested for expertise on the system with an average accuracy of diagnosis results of 94% from 13 test cases on pests and diseases of coffee plants, so it can be concluded that the system Experts can diagnose coffee plant pests and diseases very well using the Dempster Shafer method
Otomatisasi Penerusan Laporan Pengaduan Menggunakan Neural Network Khoiruddin, Lukman; Sidauruk, Acihmah; Pristyanto, Yoga; Yudiyanto, Muhammad Resa Arif; Kurniawan, Hendra
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 13, No 2 (2024): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v13i2.6662

Abstract

Saat ini terdapat sistem laporan pengaduan masyarakat yang sudah terintegrasi ke berbagai instansi. Sistem ini dikembangkan oleh Pemerintah Republik Indonesia bernama Sistem Layanan Aspirasi dan Pengaduan Online Rakyat (LAPOR!). Berdasarkan sistem LAPOR jumlah aduan yang masuk terus meningkat. Dengan adanya sistem ini berbagai aduan yang disampaikan oleh warga masyarakat dapat terintegrasi ke instansi yang berwenang menangani aduan tersebut. Dengan terintegrasinya sistem maka jumlah pengaduan yang masuk sangat banyak sehingga terdapat kendala pada saat proses verifikasi pengaduan yang nantinya akan diteruskan ke pihak yang berwenang. Tujuan penelitian ini adalah melakukan klasifikasi terhadap setiap laporan pengaduan dan mengetahui pengaruh terhadap Replace Slang Word. Dalam proses klasifikasi di penelitian ini menggunakan algoritma Artificial Neural Network. Jumlah data pengaduan yang digunakan adalah sebanyak 750 data pengaduan. Data tersebut terbagi menjadi 3 kategori yaitu bidang pendidikan, kesehatan dan infrasturktur. Untuk pembagian jumlah data dilakukan sama di setiap kategori. Pada tahapan Preprocessing menggunakan replace slang word sebagai penggati kata slang terhadap kata aslinya. Hasil dari penelitian ini adalah menunjukkan nilai tinggi terhadap nilai Accuracy yaitu sebesar 99,33% untuk F1 score, Precission, dan Recall memiliki nilai yang sama yaitu 99,09%. Dengan hasil yang tinggi maka metode yang diusulkan dapat digunakan untuk melakukan pengklasifikasian terhadap laporan pengaduan.
Co-Authors Abdullah Abdullah Abdussalam Al Akbar Achmad Lukman, Achmad Ade Pujianto Ade Pujianto, Ade Afifah Nur Latifah Afrianto, Nurdi Afrig Aminuddin Alvy Chasanatussalma Anggita, Sharazita Dyah Arif Dwi Laksito Asro Nasiri Atik Nurmasani Bernadhed, Bernadhed Bister Purba Budy Satria Burhanudin, Nazil Ilham Danang Wijayanto Darmawan, Briga Dwi Rahayu Elsafira Budi Dewantari Erni Seniwati Febri Liantoni, Febri Gita Cahyani Gunawan, Puguh Hasta Hairul Januar Haryoko, Haryoko Hasyim, Jaka Wardana Hendra Kurniawan Ihsan, M. Arinal Ikmah Ikmah Ilyas Ferry Ceasar Widayanto Jamroni Jamroni Kharisma, Rizqi Sukma Khoiruddin, Lukman Liga Mayola M. Arinal Ihsan Miftakhurrokhmat Miftakhurrokhmat, Miftakhurrokhmat Muhammad Iqbal Kusrachmansyah Muhammad Resa Arif Yudianto Mulia Sulistiyono Mulyatun, Sri Ningsih, Lidya Norhikmah Norhikmah Norhikmah, - Nurcholis, Muhammad Tofa Panggih Suseno Pirmansah, Imam Ainudin Proboningrum, Sandyea Rabiatul Adawiyah Raditya Wardhana Raditya Wardhana Rahmi, Alfie Nur Ria Andriani Riza Yulina Amry Robert Marco, Robert Rumini Rumini SAKINAH, PUTRI Salmuasih, Salmuasih Sarni Anggoro Satria, Dhimas Adi Seniwati, Erni Shofi Putri Ekadewi Sutarni, Sutarni Tri Widayanti Trisa Ayunanda Saputri Wardhana, Raditya Waskito, Bimo Wiji Nurastuti Wiji Nurastuti, Wiji Wiwi Widayani Wulandari, Evita Sekar Yoga Pristyanto Yudiyanto, Muhammad Resa Arif Yuli Astuti Yuli Astuti