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Implementation of Fuzzy Expert System to Detect Parkinson's Disease Based on Mobile Chen, Jacky; Gustientiedina, Gustientiedina
Journal of Applied Business and Technology Vol. 5 No. 2 (2024): Journal of Applied Business and Technology
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/jabt.v5i2.145

Parkinson's disease is a neurodegenerative disorder characterized by classic motor symptoms, namely bradykinesia, rigidity and tremor, where this disease attacks nerve cells gradually in the midbrain part which regulates the movement of the human body. This disease is one of the most common diseases found in old age with a prevalence of around 160 per 100,000 population. Among the general public knowledge about the diseaseparkinson considered to be minimal, as a result many sufferers parkinson which is not handled properly. Therefore the authors built an application to detect and provide information on Parkinson's disease withFuzzy Expert System. This application was built based on Android mobile to make it easier for users to operate it. In this research method Fuzzy Expert System aims to find out whether the patient has Parkinson's or not based on the input value of each symptom displayed. Symptom data were obtained from experts through interviews and appropriate literature. This system begins by entering the symptoms of Parkinson's disease that have been obtained from experts into the system. Symptoms included include: Tremor/vibration, Rigidity/Rigidity, Akinesia/Bradykinesia, Autonomic Dysfunction, Gait as if about to fall. After the symptoms are entered, the system will calculate the setFuzzy, each symptom is divided into 2 (two) criteria/sets, namely: rarely, and often. After forming the setFuzzy, The system will match the rule base obtained from the expert. The results of this system detection whether the user has Parkinson's disease or not. In building the system the author uses the waterfall method, which means sequential and systematic. The database used is the MySQL database. Testing this research using the Black Box Testing method. From the research that has been done, this system has succeeded in achieving a percentage value of 70% for accuracy results based on 20 trials from respondents, there are 6 experiments that are not in accordance with expert opinion. On testingusability obtaining a percentage of 40% for very good and 60% for good, with these results showing that the expert system that has been built can run well and is easy for users to use. Keywords: Parkinson's Disease, Detecting, Fuzzy Expert System, Mobile

Penerapan Algoritma K-Means Untuk Clustering Data Obat-Obatan Pada RSUD Pekanbaru Gustientiedina Gustientiedina; M. Hasmil Adiya; Yenny Desnelita
Jurnal Nasional Teknologi dan Sistem Informasi Vol 5 No 1 (2019): April 2019
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v5i1.2019.17-24

Perencanaan dari kebutuhan obat-obatan yang tepat dapat membuat pengadaan obat-obatan menjadi efektif dan efisien sehinggaobat-obatan dapat tersedia dengan cukup sesuai dengan kebutuhan serta dapat diperoleh pada saat yang diperlukan. Menganalisa pemakaian obat, perencanaan dan pengendalian obat-obatan dapat dilakukan pada data miningyaitu dengan clusterisasi.Metode yang akan di pakai untuk clustering data obat-obatan adalah algoritma K-Means yang mana merupakan metode clustering dengan non hirarki yang mempartisi data â€“ dataÂ kedalam cluster dimana data â€“ datadengan karakteristik sama akan dikelompokkan padasatu cluster dan data â€“ data dengan karakteristik yang berbeda akan dikelompokkan padacluster lainnya.Tujuan penelitian ini yaitumengelompokkan data obat-obatan pada rumah sakitsehingga dapat digunakan dalam acuan pengambilan keputusan perencanaan dan pengendaliaan persediaan obat-obatan di rumah sakit.

Optimalisasi Komersialisasi Produk Industri Serai Wangi UKM Siti Hajar Pekanbaru Yusrizal, Yusrizal; Desnelita, Yenny; Rahman, Sarli; Gustientiedina, Gustientiedina; Fadrul, Fadrul
Community Engagement and Emergence Journal (CEEJ) Vol. 3 No. 3 (2022): Community Engagement & Emergence Journal (CEEJ)
Publisher : Yayasan Riset dan Pengembangan Intelektual

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/ceej.v3i3.1076

Minyak serai wangi merupakan salah satu komunitas UKM Siti Hajar yang berdiri sejak tahun 2020. UKM ini memproduksi minyak serai wangi mempunyai aroma terapi yang nama produknya Citronella Oil HOMEMADE by Komunitas UKM Siti Hajar. Produk serai wangi UKM Siti Hajar selama ini belum diarahkan dan dikembangkan menjadi suatu produk bernilai jual. Permasalahan yang ditemukan dari UKM Siti Hajar yaitu dalam strategi produksi kemasan yang masih belum memuaskan karena masih dikerjakan secara manual belum memanfaatkan teknologi. Dan produk juga belum didaftarkan ke badan BPOM dan belum mempunyai sertifikat halal MUI. Jumlah produk yang dihasilkan masih kurang dalam memenuhipermintaan serta belum adanya jaminan kualitas produk; dan belum memiliki branding kemasan sebagai keunikan dan ciri khas dari usaha UKM SitiHajar terhadap minyak serai wangi. Solusi yang ditawarkan kepada UKM Siti Hajar untuk menyelesaikan masalah tersebut yaitu dengan cara: 1).pendampingan cara pemilihan, pemanfaatan bahan baku dan penggunaan mesin produksi untuk proses pengisian dan packing botol minyak serai yang higienis, 2). Mendesain packaging kemasan minyak serai wangi, dan 3) pendampingan pendaftaran produk ke Badan POM dan sertifikat Halal MUI. Kegiatan dilakukan melalui sosialisasi dan pelatihan penggunaan teknologi yang digunakan serta pendampingan izin produk minyak serai wangi. Melalui kegiatan PkM ini dapat membantu UKM Siti Hajar dalam kemampuan menggunakan teknologi dan kemampuan manajemen meningkat, serta produk memiliki sertifikat bermutu. Kata Kunci: Optimalisasi Produk, Minyak Serai Wangi, Teknologi, Manjemen, UKM Siti Hajar

HAND POSE CLASSIFICATION USING MEDIAPIPE HANDS AND CNN-LSTM FOR AUGMENTED REALITY BASED INTRAVENOUS INFUSION LEARNING Desnelita, Yenny; Siddik, Muhammad; Lita, Lita; Hajjah, Alyauma; Gustientiedina, Gustientiedina
Jurnal Testing dan Implementasi Sistem Informasi Vol. 3 No. 2 (2025): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v3i2.2343

Intravenous infusion training requires precise hand positioning and coordinated movements; however, conventional training approaches remain subjective and lack consistent real-time feedback. Moreover, existing augmented reality (AR)-based systems are largely limited to visualization and do not provide intelligent, automated skill evaluation. To address this gap, this study proposes an integrated hand pose classification framework that combines MediaPipe-based landmark extraction, CNN-LSTM spatio-temporal modeling, and AR-based feedback for real-time procedural learning. The novelty of this work lies in the seamless integration of lightweight feature representation, hybrid deep learning, and interactive AR feedback within a unified learning system. Experimental results demonstrate that the proposed approach achieves high classification performance, with an accuracy of 94.82% and an AUC of approximately 0.97, indicating strong discriminative capability. The system also operates in real time with low latency, enabling immediate feedback and adaptive learning. This study contributes theoretically to spatio-temporal gesture modeling and practically to the development of intelligent AR-based training systems. The proposed framework offers a scalable and objective solution for improving procedural accuracy, consistency, and accessibility in medical education.

HAND POSE CLASSIFICATION USING MEDIAPIPE HANDS AND CNN-LSTM FOR AUGMENTED REALITY BASED INTRAVENOUS INFUSION LEARNING Yenny Desnelita; Muhammad Siddik; Lita Lita; Alyauma Hajjah; Gustientiedina Gustientiedina
Jurnal Testing dan Implementasi Sistem Informasi Vol. 3 No. 2 (2025): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v3i2.2343

Intravenous infusion training requires precise hand positioning and coordinated movements; however, conventional training approaches remain subjective and lack consistent real-time feedback. Moreover, existing augmented reality (AR)-based systems are largely limited to visualization and do not provide intelligent, automated skill evaluation. To address this gap, this study proposes an integrated hand pose classification framework that combines MediaPipe-based landmark extraction, CNN-LSTM spatio-temporal modeling, and AR-based feedback for real-time procedural learning. The novelty of this work lies in the seamless integration of lightweight feature representation, hybrid deep learning, and interactive AR feedback within a unified learning system. Experimental results demonstrate that the proposed approach achieves high classification performance, with an accuracy of 94.82% and an AUC of approximately 0.97, indicating strong discriminative capability. The system also operates in real time with low latency, enabling immediate feedback and adaptive learning. This study contributes theoretically to spatio-temporal gesture modeling and practically to the development of intelligent AR-based training systems. The proposed framework offers a scalable and objective solution for improving procedural accuracy, consistency, and accessibility in medical education.

Implementation of Certainty Factor Method in Mental Health Diagnosis Expert System in Adolescents Aged 18 – 24 Years Yenny Desnelita; Mario Cesar; Gustientiedina Gustientiedina; Alyauma Hajjah; Ramalia Noratama Putri
Journal of Applied Business and Technology Vol. 6 No. 1 (2025): Journal of Applied Business and Technology
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/jabt.v6i1.195

Mental health in adolescents aged 18-24 years is a real condition and a problem that has received less attention from parents or certain parties. Individual adolescents free from all forms of symptoms of mental disorders are one of the keys to maintaining a healthy body from mental health disorders . The group most vulnerable to mental health disorders is adolescents, where many adolescents do not receive the care they should from their parents. Expert systems can help solve problems in the field of mental health in adolescents aged 14-18 years as befits a psychiatrist by adopting expert knowledge into computers. This study aims to develop an expert system for diagnosing mental health disorders in adolescents aged 18-24 years using the Certainty Factor (CF) method by combining expert and user belief values in the diagnostic solutions provided later by the expert system. This study used five mental disorders in adolescents aged 18-24 years, namely depression, schizophrenia, bipolar, obsessive, anxiety disorders which were later given the weight of symptom beliefs and data on preventive solutions to the disease using the CF method . The results of the study are in the form of an expert system for diagnosing mental health in adolescents. using the CF method which displays the certainty value of expert knowledge diagnosis. Testing of the expert system application in this study uses the black-box method with valid test results used.

Optimalisasi Komersialisasi Produk Industri Serai Wangi UKM Siti Hajar Pekanbaru Yusrizal Yusrizal; Yenny Desnelita; Sarli Rahman; Gustientiedina Gustientiedina; Fadrul Fadrul
Community Engagement and Emergence Journal (CEEJ) Vol. 3 No. 3 (2022): Community Engagement & Emergence Journal (CEEJ)
Publisher : Yayasan Riset dan Pengembangan Intelektual

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/ceej.v3i3.1076

Minyak serai wangi merupakan salah satu komunitas UKM Siti Hajar yang berdiri sejak tahun 2020. UKM ini memproduksi minyak serai wangi mempunyai aroma terapi yang nama produknya Citronella Oil HOMEMADE by Komunitas UKM Siti Hajar. Produk serai wangi UKM Siti Hajar selama ini belum diarahkan dan dikembangkan menjadi suatu produk bernilai jual. Permasalahan yang ditemukan dari UKM Siti Hajar yaitu dalam strategi produksi kemasan yang masih belum memuaskan karena masih dikerjakan secara manual belum memanfaatkan teknologi. Dan produk juga belum didaftarkan ke badan BPOM dan belum mempunyai sertifikat halal MUI. Jumlah produk yang dihasilkan masih kurang dalam memenuhipermintaan serta belum adanya jaminan kualitas produk; dan belum memiliki branding kemasan sebagai keunikan dan ciri khas dari usaha UKM SitiHajar terhadap minyak serai wangi. Solusi yang ditawarkan kepada UKM Siti Hajar untuk menyelesaikan masalah tersebut yaitu dengan cara: 1).pendampingan cara pemilihan, pemanfaatan bahan baku dan penggunaan mesin produksi untuk proses pengisian dan packing botol minyak serai yang higienis, 2). Mendesain packaging kemasan minyak serai wangi, dan 3) pendampingan pendaftaran produk ke Badan POM dan sertifikat Halal MUI. Kegiatan dilakukan melalui sosialisasi dan pelatihan penggunaan teknologi yang digunakan serta pendampingan izin produk minyak serai wangi. Melalui kegiatan PkM ini dapat membantu UKM Siti Hajar dalam kemampuan menggunakan teknologi dan kemampuan manajemen meningkat, serta produk memiliki sertifikat bermutu. Kata Kunci: Optimalisasi Produk, Minyak Serai Wangi, Teknologi, Manjemen, UKM Siti Hajar

Framework Smart Mushroom Farming Berbasis IoT dan AI Menggunakan Pendekatan Multimodal untuk Budidaya Jamur Merang Desnelita, Yenny; Gustientiedina, Gustientiedina; Noratama Putri, Ramalia; Hajjah, Alyauma; Nora Marlim, Yulvia; Irwan, Irwan
Jurnal Pustaka Robot Sister (Jurnal Pusat Akses Kajian Robotika, Sistem Tertanam, dan Sistem Terdistribusi) Vol 4 No 2 (2026): Jurnal Pustaka Robot Sister (Pusat Akses Kajian Robotika, Sistem Tertanam, dan Si
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakarobotsister.v4i2.2451

Budidaya jamur merang (Volvariella volvacea) sangat bergantung pada kestabilan mikroklimat yang selaras dengan tahapan pertumbuhannya, sehingga data lingkungan dan perkembangan visual jamur perlu dianalisis secara terpadu agar pengelolaan budidaya dapat dilakukan secara lebih presisi. Penelitian ini mengusulkan sebuah framework Smart Mushroom Farming berbasis Internet of Things (IoT) dan Artificial Intelligence (AI) dengan pendekatan multimodal untuk mendukung pemantauan lingkungan sekaligus analisis fase pertumbuhan jamur merang. Framework disusun melalui pendekatan Design Science Research (DSR) dan diwujudkan dalam arsitektur empat lapisan, yaitu sensing, communication, processing, dan application layer. Pada lapisan akuisisi data, parameter suhu, kelembapan relatif, konsentrasi CO₂, intensitas cahaya, dan kelembapan media dipadukan dengan citra RGB perkembangan jamur sebagai representasi kondisi visual. Seluruh data tersebut ditransmisikan melalui protokol MQTT dan dikelola dalam basis data berbasis cloud. Pada lapisan pemrosesan, pendekatan multimodal dirancang untuk menggabungkan fitur mikroklimat dengan fitur visual yang diekstraksi menggunakan Convolutional Neural Network (CNN), sebagai dasar konseptual bagi identifikasi fase pertumbuhan mulai dari miselium, primordia (tiny button), button (egg), elongation, hingga fase matang/siap panen. Framework yang diusulkan menghubungkan tahapan akuisisi data, komunikasi IoT, integrasi data multimodal, analisis berbasis AI, visualisasi, hingga keluaran pengendalian mikroklimat dalam satu arsitektur yang saling terhubung. Kontribusi utama penelitian ini terletak pada penyediaan dasar konseptual dan teknis bagi pengembangan sistem budidaya jamur merang yang adaptif dan berbasis data, yang selanjutnya dapat diuji lebih lanjut melalui tahap implementasi dan validasi eksperimental.

Disrupting the Veterinary Value Chain through AI-Based Goat Health Management Achmad Tavip Junaedi; Nicholas Renaldo; Nyoto Nyoto; Erlin Erlin; Gustientiedina Gustientiedina; Yulvia Nora Marlim; Tito Suprayoga; Jahrizal Jahrizal; Umar Faruq; Kristy Veronica
Journal of Applied Business and Technology Vol. 7 No. 2 (2026): Jounal of Applied Business and Technology
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/9x4hae67

The digital transformation of livestock production is creating new opportunities to redesign conventional veterinary service delivery. However, veterinary services in goat farming remain constrained by limited personnel, geographic accessibility, delayed disease detection, and relatively high service costs. This study examines how artificial intelligence can disrupt the veterinary value chain through Kambing Light Vision, an AI-based goat health management platform that integrates Computer Vision, disease detection, prediction confidence, and AI-assisted veterinary care recommendations. An exploratory qualitative case study approach was employed by analyzing the technology development process, veterinary service workflow, stakeholder roles, commercialization pathways, and potential business value. The results indicate that AI can shift preliminary health assessment from a predominantly veterinarian-dependent process toward a digitally assisted and farmer-accessible service. This transformation can shorten information flows, reduce service and transaction bottlenecks, improve veterinary triage, and create new value propositions for farmers, veterinary clinics, cooperatives, and agribusiness organizations. The analysis further identifies subscription-based Software as a Service, technology licensing, and managed veterinary services as potential mechanisms for converting AI capabilities into scalable business value. The study argues that the disruptive potential of AI does not lie solely in disease-classification accuracy but in its ability to reconfigure the veterinary value chain and transform how veterinary services are accessed, delivered, and commercialized. Kambing Light Vision therefore represents a transition from AI as a diagnostic technology toward AI-enabled veterinary service infrastructure with potential implications for digital agriculture and sustainable livestock business.

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