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"ABSTRACTSel tumor adalah sel yang terbentuk akibat kegagalan beberapa protein dalam mengatur siklus sel. Protein TP53 berperan penting dalam mengatur siklus sel, khususnya dalam menekan perkembangan sel tumor. Perubahan pada gen TP53 ditemukan dalam lebih dari setengah kasus tumor pada manusia. Protein lain yang berhubungan dengan protein TP53 juga ditemukan terlibat dalam proses pembentukan kanker. Analisis interaksi protein TP53 dengan melakukan clustering jaringan interaksi protein (PPI) TP53 adalah hal penting dalam membantu mengatasi sel tumor. Jaringan PPI dinyatakan sebagai graf dengan protein dan interaksinya masing-masing sebagai simpul dan busur pada graf. Spectral clustering adalah metode graph clustering yang menggunakan eigenvector dari matriks Laplacian.

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ABSTRACTFuzzy random walk adalah metode fuzzy clustering yang menggunakan probabilitas transisi dari random walk pada data. Dua metode tersebut akan digabungkan dan diimplementasikan pada penelitian ini. Menggunakan data PPI protein TP53 dari STRING database, didapat gabungan kedua metode tersebut mampu menghasilkan cluster yang fuzzy dan robust di mana setiap cluster dapat menjelaskan bagian tertentu dari fungsi protein TP53. Tumor cell is formed as a result of malfunctioning of some proteins that regulates the cell cycle. TP53 protein plays an important role in managing cell cycle, especially in tumor cell suppression. An alteration of TP53 gene is found in more than half cases of human tumor. Moreover, TP53-related proteins are also found involved in the carcinogenesis process. Therefore, it is important to analyze the interactions of TP53 protein by clustering protein-protein interactions (PPI) network of TP53. PPI networks are usually represented as a graph network with proteins and interactions as vertices and edges respectively. Spectral Clustering is a graph clustering algorithm based on eigenvector of the graph Laplacian. Fuzzy Random Walk is a fuzzy clustering method based on transition probability from a random walk on a dataset. In this paper, we combine both Spectral Clustering and Fuzzy Random Walk. Using PPI datasets of TP53 obtained from the STRING database, we found the combined algorithm is proven to produce both robust and fuzzy clusters with each cluster explains one of TP53 proteins functionality."

"ABSTRAK Latar belakang: Fibroadenoma dan tumor filodes jinak merupakan tumorfibroepitelial dengan gambaran histopatologik yang tumpang tindih. Saat inibanyak pengambilan jaringan tumor payudara secara core biopsy, termasuk padatumor fibroepitelial. Jumlah jaringan yang sedikit dan gambaran histopatologikyang tumpang tindih sering menyulitkan Dokter Spesialis Patologi Anatomikdalam menentukan diagnosis fibroadenoma dan tumor filodes jinak. Penelitian inibertujuan untuk mengetahui gambaran histopatologik apa saja yang bermaknauntuk mendiagnosis fibroadenoma dan tumor filodes jinak dan untuk mengujiapakah diagnosis fibroadenoma dan tumor filodes jinak pada core biopsy denganmenggunakan sistem skoring lebih baik dibandingan tanpa skoring.Bahan dan cara: Penelitian ini merupakan suatu uji diagnostik. 57 kasusfibroadenoma dan tumor filodes jinak yang memiliki slaid core biopsy danmastektomi/lumpektomi/eksisi dinilai ulang tanpa sistem skoring danmenggunakan skoring. Gambaran histopatologik yang dinilai pada sistem skoringadalah selularitas stroma, atipia inti, fragmentasi jaringan, infiltrasi lemak, mitosisdan heterogenitas stroma. Kemudian dilakukan analisis statistik, uji diagnostikdan uji kappa.Hasil: Selularitas stroma, heterogenitas stroma dan fragmentasi jaringan lebihsering ditemukan pada tumor filodes jinak dan berbeda bermakna (p=0,001;p=0,000; p=0,021). Spesifisitas pada sistem skoring meningkat sebesar 17,9%.Nilai duga positif dan nilai duga negatif pada sistem skoring meningkat sebesar11,9% dan 5,1%. Area under curve (AUC) meningkat 8,9%. Uji Cohen?s kappaantara diagnosis core biopsy tanpa dan dengan skoring bernilai rendah (0,545).Kesimpulan: Adanya peningkatan spesifisitas, nilai duga positif dan AUCmenunjukkan bahwa penilaian core biopsy sistem skoring lebih baikdibandingkan tanpa skoring dan dapat menjadi acuan untuk diagnosis fibroadenoma dan tumor filodes jinak.ABSTRACT Background: Fibroadenoma and benign phyllodes tumor are kinds of fibroepithelial tumor which have overlapping histopathological features. Recently, core biopsy is commonly performed to determine breast tumor, including fibroepithelial tumor. Small amount of tissue and overlapped histopathological features often complicate the Pathologist in diagnosing both. This study aims to describe the histopathological appearance which needed to diagnose fibroadenoma and benign phyllodes tumor and to verify if the diagnosis of fibroadenoma and benign phyllodes tumor in core biopsy using scoring system is more accurate than without scoring system.Method: This study was a diagnostic test, in which 57 cases of fibroadenoma and benign phyllodes tumor which had undergone core biopsy and mastectomy/excision were re-assessed using and without using scoring system. Histopathologic features which assessed using scoring system were stromal cellularity, nuclear atypia, tissue fragmentation, fat infiltration, mitotic figure, stromal heterogeneity. Analytical statistic, diagnostic test, accuracy test and Kappa test were done.Results: Stromal cellularity, stromal heterogeneity and tissue fragmentation mostly found in benign phyllodes tumor and significantly different (p=0,001; p=0,000; p=0,021).There were significant differences between stromal cellularity (p=0,001), stromal heterogeneity (p=0,000), and tissue fragmentation (p=0,021) in diagnosis of benign phyllodes tumor. Specificity in scoring system increased by17,9 %. Positive predictive value, negative predictive value and accuracy increased in scoring system (11,9% and 5,1%). Area under curve (AUC) increased by 8,9%. Cohen's Kappa test between core biopsy diagnosis without using and using scoring system had low result(0,545). Conclusion: The increasing of specificity, positive predictive value, accuracy and AUC proved that core biopsy with scoring system is more accurate than without scoring. This can be used as reference to diagnose fibroadenoma and benign phyllodes tumor.;Background: Fibroadenoma and benign phyllodes tumor are kinds of fibroepithelial tumor which have overlapping histopathological features. Recently, core biopsy is commonly performed to determine breast tumor, including fibroepithelial tumor. Small amount of tissue and overlapped histopathological features often complicate the Pathologist in diagnosing both. This study aims to describe the histopathological appearance which needed to diagnose fibroadenoma and benign phyllodes tumor and to verify if the diagnosis of fibroadenoma and benign phyllodes tumor in core biopsy using scoring system is more accurate than without scoring system.Method: This study was a diagnostic test, in which 57 cases of fibroadenoma and benign phyllodes tumor which had undergone core biopsy and mastectomy/excision were re-assessed using and without using scoring system. Histopathologic features which assessed using scoring system were stromal cellularity, nuclear atypia, tissue fragmentation, fat infiltration, mitotic figure, stromal heterogeneity. Analytical statistic, diagnostic test, accuracy test and Kappa test were done.Results: Stromal cellularity, stromal heterogeneity and tissue fragmentation mostly found in benign phyllodes tumor and significantly different (p=0,001; p=0,000; p=0,021).There were significant differences between stromal cellularity (p=0,001), stromal heterogeneity (p=0,000), and tissue fragmentation (p=0,021) in diagnosis of benign phyllodes tumor. Specificity in scoring system increased by17,9 %. Positive predictive value, negative predictive value and accuracy increased in scoring system (11,9% and 5,1%). Area under curve (AUC) increased by 8,9%. Cohen's Kappa test between core biopsy diagnosis without using and using scoring system had low result(0,545). Conclusion: The increasing of specificity, positive predictive value, accuracy and AUC proved that core biopsy with scoring system is more accurate than without scoring. This can be used as reference to diagnose fibroadenoma and benign phyllodes tumor.;Background: Fibroadenoma and benign phyllodes tumor are kinds of fibroepithelial tumor which have overlapping histopathological features. Recently, core biopsy is commonly performed to determine breast tumor, including fibroepithelial tumor. Small amount of tissue and overlapped histopathological features often complicate the Pathologist in diagnosing both. This study aims to describe the histopathological appearance which needed to diagnose fibroadenoma and benign phyllodes tumor and to verify if the diagnosis of fibroadenoma and benign phyllodes tumor in core biopsy using scoring system is more accurate than without scoring system.Method: This study was a diagnostic test, in which 57 cases of fibroadenoma and benign phyllodes tumor which had undergone core biopsy and mastectomy/excision were re-assessed using and without using scoring system. Histopathologic features which assessed using scoring system were stromal cellularity, nuclear atypia, tissue fragmentation, fat infiltration, mitotic figure, stromal heterogeneity. Analytical statistic, diagnostic test, accuracy test and Kappa test were done.Results: Stromal cellularity, stromal heterogeneity and tissue fragmentation mostly found in benign phyllodes tumor and significantly different (p=0,001; p=0,000; p=0,021).There were significant differences between stromal cellularity (p=0,001), stromal heterogeneity (p=0,000), and tissue fragmentation (p=0,021) in diagnosis of benign phyllodes tumor. Specificity in scoring system increased by17,9 %. Positive predictive value, negative predictive value and accuracy increased in scoring system (11,9% and 5,1%). Area under curve (AUC) increased by 8,9%. Cohen's Kappa test between core biopsy diagnosis without using and using scoring system had low result(0,545). Conclusion: The increasing of specificity, positive predictive value, accuracy and AUC proved that core biopsy with scoring system is more accurate than without scoring. This can be used as reference to diagnose fibroadenoma and benign phyllodes tumor.;Background: Fibroadenoma and benign phyllodes tumor are kinds of fibroepithelial tumor which have overlapping histopathological features. Recently, core biopsy is commonly performed to determine breast tumor, including fibroepithelial tumor. Small amount of tissue and overlapped histopathological features often complicate the Pathologist in diagnosing both. This study aims to describe the histopathological appearance which needed to diagnose fibroadenoma and benign phyllodes tumor and to verify if the diagnosis of fibroadenoma and benign phyllodes tumor in core biopsy using scoring system is more accurate than without scoring system.Method: This study was a diagnostic test, in which 57 cases of fibroadenoma and benign phyllodes tumor which had undergone core biopsy and mastectomy/excision were re-assessed using and without using scoring system. Histopathologic features which assessed using scoring system were stromal cellularity, nuclear atypia, tissue fragmentation, fat infiltration, mitotic figure, stromal heterogeneity. Analytical statistic, diagnostic test, accuracy test and Kappa test were done.Results: Stromal cellularity, stromal heterogeneity and tissue fragmentation mostly found in benign phyllodes tumor and significantly different (p=0,001; p=0,000; p=0,021).There were significant differences between stromal cellularity (p=0,001), stromal heterogeneity (p=0,000), and tissue fragmentation (p=0,021) in diagnosis of benign phyllodes tumor. Specificity in scoring system increased by17,9 %. Positive predictive value, negative predictive value and accuracy increased in scoring system (11,9% and 5,1%). Area under curve (AUC) increased by 8,9%. Cohen's Kappa test between core biopsy diagnosis without using and using scoring system had low result(0,545). Conclusion: The increasing of specificity, positive predictive value, accuracy and AUC proved that core biopsy with scoring system is more accurate than without scoring. This can be used as reference to diagnose fibroadenoma and benign phyllodes tumor."

"ABSTRAK Latar belakang: Sarkoma Ewing merupakan suatu small round cell tumor yang ditandai dengan fusi gen EWSR1/FLI1 pada 85 kasus. Diagnosis akurat diperlukan karena memiliki respon baik terhadap protokol kemoterapi spesifik. Baku emas diagnosis sarkoma Ewing adalah deteksi translokasi spesifik dengan RT-PCR atau FISH, namun pemeriksaan tersebut belum tersedia di institusi kami, sehingga dilakukan upaya lain untuk mempertajam diagnosis. Secara morfologi, sarkoma Ewing sering overlapping dengan small round cell tumor lainnya. Pulasan CD99 merupakan penanda yang sangat sensitif untuk mendiagnosis sarkoma Ewing, namun juga sering overlapping dengan small round cell tumor lainnya. Beberapa penelitian mengemukakan FLI1 dapat digunakan sebagai penanda diagnosis sarkoma Ewing. Tujuan penelitian ini adalah menilai ekspresi FLI1 untuk membantu menegakkan diagnosis sarkoma Ewing pada small round cell tumor yang memberikan hasil positif terhadap CD99, terutama pada kasus biopsi.Bahan dan cara: Penelitian ini menggunakan desain potong lintang. Sampel terdiri atas 36 kasus sarkoma Ewing dan 18 kasus small round cell tumor yang sudah dilakukan pulasan imunohistokimia CD99 di RSCM dari Januari 2011 sampai Mei 2018. Dilakukan pulasan FLI1 dan penilaian menggunakan H-score.Hasil: Titik potong H-score pada ekspresi FLI1 didapatkan 226.1 75 dengan sensitivitas 81.6 dan spesifisitas 94.4 . Ekspresi FLI1 tinggi didapatkan pada 31 kasus sarkoma Ewing, sedangkan pada 18 kasus small round cell tumor umumnya memiliki ekspresi FLI1 yang rendah ABSTRACT Background: Ewing 39;s sarcoma is a small round cell tumor characterized by EWSR1 / FLI1 gene fusion in 85 of cases. Accurate diagnosis is necessary because it has a good response to a specific chemotherapy protocol. The gold standard of Ewing 39;s sarcoma diagnosis is the detection of specific translocation with RT-PCR or FISH, but the examination is not yet available at our institution, so another attempt is made to sharpen the diagnosis. Morphologically, Ewing 39;s sarcoma is often overlapping with other small round cell tumor. CD99 is a very sensitive marker for diagnosing Ewing 39;s sarcoma, but also often overlapping with other small round cell tumors. Several studies have suggested that FLI1 can be used as a marker of Ewing rsquo;s sarcoma. The purpose of this study was to assess the FLI1 expression to help establish the diagnosis of Ewing rsquo;s sarcoma in small round cell tumors that gave CD99 positive results, especially in the biopsy cases. Materials and methods: This was a cross-sectional study with 36 cases of Ewing rsquo;s sarcoma and 18 cases of other small round cell tumor that had been performed CD99 immunohistochemistry at RSCM from January 2011 to May 2018. All cases stained by FLI1 antibody and evaluated using H-score. Results: The H-score cut-off point on FLI1 expression was obtained at 226.1 75 with 81.6 sensitivity and 94.4 specificity. The high FLI1 expression was obtained in 31 cases of Ewing rsquo;s sarcoma, while in 18 cases of small round cell tumor were generally had low expression of FLI1 p"

"Pembentukan dan perkembangbiakan sel tumor terjadi jika protein khusus yang mengatur pembelahan sel mengalami perubahan fungsi, ekspresi gen atau hilang keduanya. Salah satu protein penekan tumor yang berperan dalam pengendalian siklus sel adalah protein TP53. Pada sebagian besar perubahan genetik dalam tumor, baik delesi atau mutasi pada lebih dari 50% kanker pada manusia, ditemukan mutan TP53 yang merupakan faktor beresiko tinggi terhadap kanker. Oleh karena itu, penting untuk melakukan studi tentang pengelompokan interaksi protein-protein TP53. Interaksi protein secara umum disajikan dalam jaringan graf (graph network) dengan protein sebagai simpul dan interaksinya sebagai busur. Algoritma Markov Clustering (MCL) adalah satu metode graph clustering yang dibuat berdasarkan simulasi dari flow stokastik pada suatu graf. Dalam skripsi ini, dibahas mengenai implementasi algoritma MCL pada data interaksi protein-protein TP53 dengan menggunakan bahasa pemrograman Python. Algoritma MCL terdiri dari tiga operasi utama yaitu ekspansi, penggelembungan, dan pemotongan. Selanjutnya, dilakukan analisis hasil clustering dari simulasi algoritma MCL dengan menggunakan parameter ekspansi, penggelembungan dan faktor pengali yang berbeda-beda. Berdasarkan analisis hasil clustering yang dilakukan, algoritma MCL terbukti menghasilkan robust cluster dengan protein TP53 sebagai pusat cluster untuk setiap hasil clustering.

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The formation and proliferation of tumor cells occurs if a special protein that regulates cell division changing the function, gene expression or lost both. One of the tumor suppressor protein that plays a role in controlling the cell cycle is the TP53 protein. In most of the genetic changes in the tumor, either deletions or mutations in more than 50% of human cancers, it found that mutant of TP53 is a high risk factor for cancer. Therefore, it is important to conduct studies on protein-protein interactions clustering of TP53. Protein interactions are generally presented in the graph network with proteins as nodes and interactions as edges. Markov Clustering (MCL) algorithm is a graph clustering method which is based on a simulation of stochastic flow on a graph. This minithesis discussed about the implementation of the MCL process on protein-protein interaction of TP53 data using the Python programming language. MCL algorithm consists of three main operations: expansion, inflation, and prune. Furthermore, the clustering simulation is using the different parameter of expansion, inflation and the multiplier factor. Based on the analysis of the clustering results, MCL algorithm is proven to produce robust cluster with TP53 protein as a centroid for each clustering results."

"Pada jaringan interaksi protein-protein terdapat beberapa protein yang menjadi pusat cluster, dimana protein-protein tersebut merupakan protein yang memegang peranan penting dalam sebuah fungsi seluler. Salah satu algoritma yang dewasa ini sering digunakan untuk melakukan pencarian pusat kluster adalah algoritma Markov Clustering (MCL). Algoritma Regularized Markov Clustering (R-MCL) merupakan algoritma modifikasi MCL yang bertujuan untuk mencari pusat kluster dengan mensimulasikan random walk dalam graf interaksi protein-protein dengan menggunakan operasi ekspansi namun tetap mempertahankan topologi awal dari graf. Komputasi paralel diperlukan dalam menyelesaikan proses klusterisasi ini sebab R-MCL melibatkan data yang berukuran besar dan mengandung proses yang memiliki kompleksitas waktu yang besar. Dalam skripsi ini akan dibahas mengenai konstruksi algoritma paralel R-MCL menggunakan bahasa pemrograman CUDA C pada GPU. Data disimpan dalam format yang lebih hemat memori yaitu format data sparse ELLPACK-R yang sesuai untuk komputasi pada GPU. Algoritma paralel ini akan diimplementasikan pada mesin manycore dengan menggunakan NVCC compiler.

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There are some proteins in protein-protein interaction network that act as the cluster centers because of the important roles they have related to cellular functions. One of the clustering algorithms that are often used in clustering is Markov Clustering Algorithm (MCL). Regularized Markov Clustering (R-MCL) algorithm is a modification of MCL in order to get better results by simulating random walk in the graph using expansion operation while maintaining the original topology of the graph. Parallel computation is needed to solve this clustering problem because R-MCL algorithm uses a big number of data and contains some operations with very big time complexities. The problem that will be discussed in this minithesis is the construction of parallel R-MCL algorithm using CUDA C on GPU. The PPI data will be converted into a more memory-friendly format, in this case in ELLPACK-R sparse data format that is suitable for GPU computation. This parallel algorithm will be implemented using a manycore machine with NVCC compiler installed on it."

"ABSTRAKPenelitian ini mengimplementasikan metode spectral clustering-Fuzzy C-Means pada tiga microarray data ekspresi gen, dengan tujuan untuk mengelompokkan gen-gen yang memiliki tingkat ekspresi yang similar. Spectral clustering secara teoritis terdiri dari tiga tahap utama yaitu: membangun matriks jarak, membentuk matriks Laplacian, dan proses partisi, khususnya dalam tesis ini menggunakan algoritma partisi Fuzzy C-Means. Oleh karena itu, implementasi dari spectral clustering-FCM lebih sederhana dan intuitif pada pelaksanaannya. Analisis cluster singkat juga akan dipaparkan untuk masing-masing microarray data yang digunakan yaitu: Carcinoma, Leukemia, dan Lymphoma. Hasil cluster yang sangat baik didapatkan, sehingga metode yang diusulkan memiliki potensi besar ke depannya dalam penelitan pada bidang medis.

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ABSTRACTThis research implements the spectral clustering FCM method on three microarray gene expression data, with the aim of grouping genes with similar expression levels. Spectral clustering is theoretically composed of three main stages building distance matrix, forming Laplacian matrix, and partitioning process, especially in this thesis using Fuzzy C Means partition algorithm. Therefore, the implementation of spectral clustering FCM is simpler and more intuitive in its implementation. Brief cluster analysis will also be presented for each microarray data used Carcinoma, Leukemia, and Lymphoma. Excellent cluster results are obtained, so the proposed method has great potential for future research in the medical field. "

" ABSTRAKClustering adalah salah satu topik penting pada bidang Data Mining. Teori graf dapat digunakan untuk membantu clustering dengan cara membuat graf yang mewakili data-data yang akan di-cluster. Salah satu metode graf clustering adalah k-way spectral clustering yang memanfaatkan sebanyak k nilai eigen dan vektor eigen pertama dari matriks Laplacian suatu graf untuk melakukan clustering dengan k adalah banyaknya cluster yang diinginkan. Pada skripsi ini dibahas mengenai algoritma k-way spectral clustering merujuk kepada Ng, Jordan, dan Weiss (2002) dan von Luxburg (2007).

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ABSTRACT

Clustering is one of the most important topic in Data Mining. Graph can be used to do clustering by forming a representation graph data which is needed to be clustered. K-way spectral clustering is one of many methods of graph clustering. This method uses first-k eigen values and eigen vectors of a Laplacian matrix to cluster with k is the number of desired clusters. In this skripsi, it will be discussed a k-way spectral clustering algorithm by Ng, Jordan, and Weiss (2002) and von Luxburg (2007)."

"Latar belakang: CTC sebagai bagian dari liquid biopsy berperan dalam melakukan monitoring pasien kanker payudara yang menjalani terapi. Adanya CTC menjadi pertanda resistensi terapi dan memengaruhi prognosis pasien. Penelitian ini bertujuan melihat adakah perubahan nilai CTC pada pasien kanker payudara stadium lokal lanjut atau lanjut yang mendapatkan kemoterapi serta melihat perubahan nilai CTC tersebut apakah dipengaruhi oleh usia, status menopause, subtipe, metastasis, dan grade.Metode: Didapatkan 30 sampel pasien kanker payudara stadium lokal lanjut atau lanjut yang akan mendapatkan kemoterapi berbasis Anthracycline dan Taxan. Pre kemoterapi pasien diambil darah perifer dan dilakukan pemeriksaan CTC menggunakan flowcytometry dengan antibodi EpCAM. Pasien lalu menjalani siklus kemoterapi hingga lengkap. Setelah itu pasien kembali diambil darah perifer dan diperiksa nilai CTC post kemoterapi.Hasil: Dari ke 30 sampel, didapatkan mean usia 47,93+7.30. Sebanyak 56,7 (n=17) belum menopause, 43,3% status tumor T3 dan T4, status kelenjar getah bening terbanyak adalah N0 dan N1 (43,3%). Hanya 2 pasien yang ditemukan ada metastasis. 56,7% pasien dengan grade 3, dan subtipe terbanyak adalah luminal B ( 63,4%, n=19). Terdapat 22 pasien (73,3%) dengan ER positif, 14 pasien (46,7%) dengan PR positif. Terdapat 11 pasien (36,7%) dengan Her2 positif dan 21 pasien (70%) dengan Ki67 high proliferation. Hasil CTC pre kemoterapi didapatkan nilai median 1460,50 sedangkan CTC post kemoterapi didapatkan nilai median 415,50 dilakukan uji Wilcoxon dan perbedaan bermakna dengan nilai p=0,002. Analisis multivariat regresi linier dihubungkan antara penurunan nilai CTC terhadap usia, status menopause, subtipe, metastasis, dan grading didapatkan status menopause berhubungan bermakna terhadap perubahan nilai CTC (p<0,05).Kesimpulan: CTC pada pasien kanker payudara stadium lokal lanjut dan lanjut setelah kemoterapi lebih rendah bermakna dibandingkan sebelum kemoterapi. Status menopause memiliki hubungan bermakna terhadap penurunan jumlah CTC setelah kemoterapi pada kanker payudara stadium lokal lanjut dan lanjut

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.

Background: As part of liquid biopsy, CTCs play a role in monitoring breast cancer patients undergoing therapy. The existence of CTCs is a sign of therapy resistance and affects patient prognosis. This study aims to examine whether there are changes in CTC values in patients with locally advanced or advanced breast cancer, who receive chemotherapy and are influenced by age, menopause status, subtype, metastasis, and grade.

Method: Of the 30 samples of locally advanced or advanced breast cancer patients receiving Anthracycline and Taxan-based chemotherapy were obtained. Pre-chemotherapy, peripheral blood, was drawn and CTCs were examined using flow cytometry with EpCAM antibody. Patients then undergo a complete chemotherapy cycle. After that, the patients were again taken peripheral blood and examined for post-chemotherapy CTC values.

Result: The study was conducted at Cipto Mangunkusumo Hospital, started from December 2022 to December 2023. Of the 30 samples with the mean age was 47,93+7,30. A total of 56,7 (n=17) were not menopause, 43,3% of tumor status with the T3 and T4, and the most common lymph node status with the N0 and N1 (43,3%). Only two patients were found to have metastasis. Then, 56,7% of patients had grade 3, and the most common subtype was luminal B (63,4%, n=19). There were 22 patients (73,3%) with ER positive, 14 patients (46,7%) with PR positive, 11 patients (36,7%) with Her2 positive, and 21 patients (70%) with Ki67 high proliferation. Pre-chemotherapy CTC results obtained a median value of 1460.50, Meanwhile, post-chemotherapy CTC obtained a median value of 415,50. Wilcoxon test was performed and the difference was significant with a value of p = 0,002. Multivariate linear regression analysis was correlated between the decrease in CTC values with age, menopause status, subtype, metastasis, and grading. The menopausal status has a significant association with decrease CTC values (p<0,05).

Conclusion: CTC in locally advanced and advanced breast cancer patients after chemotherapy was significantly lower than before chemotherapy. menopause status has a significant association with decreased CTC values after chemotherapy in locally advanced and advanced breast cancer."

"Saat ini, data dalam bentuk teks semakin berlimpah pada berbagai domain dan media, baik media cetak maupun online. Penambahan kumpulan dokumen teks ini menyebabkan kemudahan akses suatu informasi atau pengetahuan yang ada pada teks semakin berkurang. Selain itu, informasi atau pengetahuan yang ada tersebut semakin sulit untuk diinterpretasi dan dipahami secara menyeluruh. Untuk itu diperlukan suatu cara untuk membantu mempermudah pemahaman suatu data teks. Hal ini dengan melakukan penggalian pengetahuan pada data teks yang melimpah melalui pemrosesan data yang tidak terstruktur (text mining), dengan mengembangkan metode interpretasi berbasis ontologi pada teks untuk memperoleh pengetahuan baru sebagai state of the art. Dalam penelitian ini, dikembangkan beberapa teknik /metode. Pertama adalah pengembangan teknik preprocessing pada data teks (korpus) serta key phrase extraction menggunakan AST (Annotated Suffix Tree) untuk memperoleh key phrase (frasa kunci) dan frekuensi kemunculan. Kedua adalah pengembangan pemodelan ontologi sebagai basis pengetahuan pada suatu domain berupa relasi antar key phrase menggunakan clustering dan Bayesian Network. Ketiga adalah pengembangan metode sparse clustering pada data sparse, yaitu is-FADDIS (iterative scaling Additive Fuzzy Spectral Clustering) untuk proses pemilahan data teks, yang merupakan pengembangan dari metode clustering FADDIS (Additive Fuzzy Spectral Clustering) serta keempat adalah pengembangan metode matching dan correlating terhadap ontologi, sebagai teknik yang digunakan saat interpretasi teks. Secara terintegrasi, pembangunan ontologi dari teks, dengan domain berita, dilakukan diawal dengan tahapan ekstraksi key phrase, clustering (is-FADDIS, opsional) dan structure learning untuk membentuk ontologi-tree. Key phrase sebagai konsep, menjadi node pada ontologi tersebut, yang menjadi basis pengetahuan domain. Tahapan berikutnya adalah melakukan interpretasi teks pada suatu teks input yang terdiri dari satu key phrase atau satu cluster menggunakan ontologi tersebut untuk mendapatkan pengetahuan baru. Interpretasi dilakukan dengan ontologi berasal dari teks dengan dua domain dan satu domain. Hasil interpretasi teks menggunakan ontologi berbasis Additive Fuzzy Spectral Clustering (is-FADDIS) ini dievaluasi menggunakan usulanscore relevansi. Pada teks input dengan satu key phrase sejumlah lima input yang diinterpretasi, hasilnya adalah 40% relevan, 40% kurang relevan dan 20% tidak relevan. Pada teks input satu cluster sejumlah dua input yang diinterpretasi, hasilnya adalah relevan. Nilai score relevansi yang relevan, secara empiris adalah lebih 0,3 dari skala 1, dan score relevansi yang didapat, ada yang mencapai 0,33. Dengan pembandingan hasil interpretasi melalui variasi teknik pada pembangunan ontologi, didapatkan, penggunaan ontologi berbasis is-FADDIS untuk interpretasi teks, relatif pada penelitian ini belum memberikan hasil optimal. Dalam penggunaan teknik-teknik yang dikembangkan, metode ini memberikan keluaran interpretasi teks yang dapat membantu untuk mengolah informasi teks dalam jumlah tidak terlalu besar tetapi cepat.

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Currently, the data in the form of text more abundant on various domains and media, both print and online media. The addition of this text document causes the ease of access to any information or knowledge contained in the text is reduced. In addition, the existing information or knowledge is increasingly difficult to interpret and understand comprehensively. For that background, the purpose of the research is to extract knowledge on abundant text data through the processing of unstructured data (text mining), by developing ontology-based interpretation method on text to gain a new knowledge as state of the art.

In this research, some technique/method were developed. The first is the development of preprocessing techniques on text data (corpus) and key phrase extraction using AST (Annotated Suffix Tree) to obtain key phrase and frequency of occurrence. The second is the development of ontology modeling as a knowledge base on a domain in the form of relationships between key phrases using Bayesian Network. The third is the development of sparse clustering method in sparse data, namely is-FADDIS (iterative scaling-Additive Fuzzy Spectral Clustering) for text grouping process, which is the addition of FADDIS clustering method (Additive Fuzzy Spectral Clustering) and the fourth is the development of matching and correlating method as a technique used at interpreting the text entered using ontology.

In an integrated manner, the ontology development of the text, with news domains, is done by processes include key phrase extraction, clustering (is-FADDIS, optional) and structure learning to form ontology-tree. Key phrase as a concept, being the node on the ontology, which becomes the domain knowledge base. The next step is to interpret the text on an input text consisting of a key phrase or a cluster using the ontology to gain new knowledge. Interpretation done with ontology comes from text with two domains and one domain. Text interpretation results using Fuzzy Spectral Clustering (is-FADDIS) based ontology is evaluated using relevancy scores.

In the input text with one key phrase a total of five interpreted inputs, the result is 40% relevant, 40% less relevant and 20% irrelevant. In one-cluster input text a number of two inputs are interpreted, the result is relevant. Relevant relevance score score, empirically more than 0.3 of scale 1, and score relevance obtained, some reaching 0.33. By comparing the results of interpretation through the variation of techniques on ontology development, it was found, the use of FADDIS-based ontology for textual interpretation, relative to this research has not provided optimal results. In the use of developed techniques, this method provides textual interpretation output that can help to process text information in quantities not too large but fastly."

"ABSTRAKMengolah data dalam bentuk graf dapat dilakukan dengan cara clustering graf, yaitu mengelompokkan graf ke dalam cluster-cluster dimana data pada satu cluster memiliki karakter yang relatif sama. Two way spectral clustering adalah salah satu cara clustering graf yang menggunakan informasi dari dua nilai eigen untuk mendapatkan dua cluster setiap melakukan proses clustering. Pada skripsi ini akan dibahas bagaimana cara clustering graf dengan metode two way spectral clustering berdasarkan kriteria partisi graf dan akan dilakukan simulasi untuk melihat hasil clustering menggunakan graf terhubung dan graf tidak terhubung.

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ABSTRACT

Data processing of graph data can be done by graph clustering, where data are grouped into clusters which data on each cluster have the similar characteristic. Two way spectral clustering is one of a graph clustering which using the smallest two eigenvalues to obtain two clusters. This skripsi will discuss how to clustering graph with two way spectral clustering method based on graph partitioning criteria and moreover data simulations will be conducted to see the results of clustering using a connected and disconnected graphs."